Network architecture and methods for location services

ABSTRACT

UE location determined by collecting and preprocessing signal data at a detector and sending extracted data to a remote locate server. The detector buffers samples from signals provided by receive channels, detects known reference signals from receive channels based on reference signal parameters, isolates symbols carrying the reference signal from frames, extracts data from symbols, and sends extracted data to locate server. The locate server receives the extracted data, estimates locate observables based on the extracted data and calculates the UE location based on the estimated locate observables, the reference signal parameters and the extracted data. The detector and/or the server may also generate correlation coefficients between reference signals carrying spectrum received from a serving cell and utilize the correlation coefficients to cancel a serving cell signal in symbols that include known in advance reference signals from the serving cell and one or more neighboring cells of the wireless system.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/578,340, filed Oct. 27, 2017, and is acontinuation-in-part of U.S. patent application Ser. No. 15/501,169,filed Feb. 1, 2017, which is a National Stage application ofInternational Application No. PCT/US2015/043321, filed Jul. 31, 2015,and claims the benefit of U.S. Provisional Patent Application No.62/032,371, filed Aug. 1, 2014.

TECHNICAL FIELD

The present embodiment relates to wireless communications and wirelessnetworks systems and systems for a Radio Frequency (RF)-basedidentification, tracking and locating of objects, including RTLS (RealTime Locating Service), LTE based locating services, andLocation-as-a-Service (LaaS).

BACKGROUND

RF-based identification and location-finding systems for determinationof relative or geographic position of objects are generally used fortracking single objects or groups of objects, as well as for trackingindividuals. Conventional location-finding systems have been used forposition determination in an open, outdoor environment. RF-based, GlobalPositioning System (GPS)/Global Navigation Satellite System (GNSS), andassisted GPSs/GNSSs are typically used. However, conventionallocation-finding systems suffer from certain inaccuracies when locatingthe objects in closed (i.e., indoor) environments, as well as outdoors.

Cellular wireless communication systems provide various methods oflocating user equipment (UE) position indoors and in environments thatare not well suited for GPS. The most accurate methods are positioningtechniques that are based on the multilateration/trilateration methods.For example, LTE (Long Term Evolution) standard release 9 specifies theDL-OTDOA (Downlink Observed Time Difference of Arrival) and release 11specifies the U-TDOA (Uplink Time Difference of Arrival) techniques thatare derivatives of the multilateration/trilateration methods.

Since time synchronization errors impact locate accuracy, thefundamental requirement for multilateration/trilateration based systemsis the complete and precise time synchronization of the system to asingle common reference time. In cellular networks, the DL-OTDOA and theU-TDOA locating methods also require, in the case of DL-OTDOA, thattransmissions from multiple antennas be time synchronized, or in thecase of U-TDOA, that multiple receivers be time synchronized.

The LTE standards release 9 and release 11 do not specify the timesynchronization accuracy for the purpose of locating, leaving this towireless/cellular service providers. On the other hand, these standardsdo provide limits for the ranging accuracy. For example, when using 10MHz ranging signal bandwidth, the requirement is 50 meters @67%reliability for the DL-OTDOA and 100 meters @67% reliability for theU-TDOA.

The above noted limits are the result of a combination of rangingmeasurements errors and errors caused by the lack of precisionsynchronization, e.g. time synchronization errors. From the relevant LTEtest specifications (3GPP TS 36.133 version 10.1.0 release 10) and otherdocuments, it is possible to estimate the time synchronization error,assuming that the synchronization error is uniformly distributed. Onesuch estimate amounts to 200 ns (100 ns peak-to-peak). It should benoted that the Voice over LTE (VoLTE) functionality also requirescellular network synchronization down to 150 nanoseconds (75 nspeak-to-peak), assuming that the synchronization error is uniformlydistributed. Therefore, going forward, the LTE network's timesynchronization accuracy will be assumed to be within 150 ns.

As for distance location accuracy, FCC directive NG 911 specifies locateaccuracy requirements of 50 meters and 100 meters. However, for theLocation Based Services (LBS) market, the indoors location requirementsare much more stringent—3 meters @67% reliability. As such, the rangingand locate error introduced by the time synchronization error of 150 ns(the standard deviation of 43 ns) is much larger than the 3 metersranging error (standard deviation of 10 ns).

While a cellular network's time synchronization might be adequate tosatisfy the mandatory FCC NG E911 emergency location requirements, thissynchronization accuracy falls short of the needs of LBS or RTLS systemusers, who require significantly more accurate locating. Thus, there isa need in the art for mitigating the locate error induced by lack ofaccurate time synchronization for cellular/wireless networks for thepurpose of supporting LBS and RTLS.

While LTE 4G deployments support Location Based Service (LBS) thatutilize available location information of the terminal, proposed 5Gdeployment are going to replace LBS support with Location-as-a-Service(LaaS) data delivery. In accordance with this model, location data willbe collected by communication service carriers and made available toclient through APIs. This approach is intended to better protect theprivacy of physical location data for enterprise customers andconsumers, but it raises a number of issues as well.

Positioning in 5G networks can be carried out either within UE devices(device-centric) or in a network-centric manner, or in both (as per LTEstandards). However, the network-centric option has several advantagesover other approaches. First, network-centric positioning enables moreadvanced positioning technology to be utilized and updated withcomputational capabilities greatly exceeding those available in UEdevices. Network-centric positioning also makes it easier to upgradepositioning algorithms and make other changes. Positioning within UEdevices tends to create logistical burdens (for carriers) because thecarriers are dependent on the device manufacturers to implementnecessary changes and the computational resources of the devices aremore limited. Furthermore, the network-centric option allows thepositioning engine to be continuously running in the background enablingubiquitous high-accuracy positioning that provide up-to-date locationinformation with a low latency, while also allowing location informationto be obtained everywhere under the network coverage area, includingindoors and within urban corridor environments. And, because thenetwork-centric option relieves the UEs of the heavy computationalburden required for positioning, network-centric positioning is moreenergy efficient from the device's perspective; achieving significant UEpower consumption improvements.

In general, wireless networks utilize two positioning methods: uplinkObserved Time Difference of Arrival (OTDOA) and downlink OTDOA. Theuplink method makes use of signals that are transmitted by the UE to thenetwork elements and the downlink method makes use of signals that aretransmitted from the network elements to the UE. Specifically, thesenetwork elements are the various types of cells (macro, small,distributed, etc.) and the signals are reference signals. Each methodhas its own tradeoffs and, depending upon the environment, one methodcan be more advantageous than another. Both methods rely on themultilateration locate method. However, in case of the downlink OTDOA,the cell-tower transmit power is two orders magnitude higher than the UEtransmitter power. As a result, the downlink OTDOA method provides moreubiquitous coverage. At the same time, uplink OTDOA may also support theAngle of Arrival (AoA) method, where AoA is measured based on uplinktransmissions from the UE and the known configuration of the eNodeBantenna array. This approach has a potential for a higher locateaccuracy, but that accuracy depends heavily on characteristics of aparticular cell's antennas. On the other hand, the uplink OTDOA methodis inherently network-centric as the relevant UE transmissions can bereceived and collected by the network elements of the cell.

Traditional implementations of the downlink OTDOA are either UE assisted(e.g., 3GPP 36.305 v14) or UE based. In the UE assisted scheme, the UEmust perform the downlink timing measurements. These measurementsinvolve estimating the Reference Signals Time Difference (RSTD) betweenreference signals from several cells such as eNodeB element. Those timedifferences are then reported to a network element called the EvolvedServing Mobile Location Center (E-SMLC) for further processing. As aresult, a considerable amount of technology is required to mitigate thedownlink timing measurements errors that are caused by the RFpropagation phenomena. Otherwise, the UE positioning accuracy isseverely impacted. However, mitigating such errors requires employingstate of the art locating signal processing technology, which comes at acost of heavy computational burden on the UE resources. There istherefore a need to improve downlink OTDOA performance without requiringthe UEs to have greater computational resources and without creatinglogistical burdens for carriers and UR manufacturers.

SUMMARY

The present disclosure relates to methods and systems for RadioFrequency (RF)-based identification, tracking and locating of objects,including Real Time Locating Service (RTLS) systems that substantiallyobviate one or more of the disadvantages associated with existingsystems. The methods and systems can use partially synchronized (intime) receivers and/or transmitters. According to an embodiment,RF-based tracking and locating is implemented in cellular networks, butcould be also implemented in any wireless system and RTLS environments.The proposed system can use software implemented digital signalprocessing and software defined radio technologies (SDR). Digital signalprocessing (DSP) can be used as well.

One approach described herein employs clusters of receivers and/ortransmitters precisely time synchronized within each cluster, while theinter-cluster time synchronization can be much less accurate or notrequired at all. The present embodiment can be used in all wirelesssystems/networks and include simplex, half duplex and full duplex modesof operation. The embodiment described below operates with wirelessnetworks that employ various modulation types, including OFDM modulationand/or its derivatives. Thus, the embodiment described below operateswith LTE networks and it is also applicable to other wirelesssystems/networks.

As described in one embodiment, RF-based tracking and locating isimplemented on 3GPP LTE cellular networks will significantly benefitfrom the precisely synchronized (in time) receivers and/or transmittersclusters. The proposed system can use software- and/orhardware-implemented digital signal processing.

As described in embodiments, a network-centric architecture supportsLaaS data delivery, and which is designed for 5G and other networks whenall signal processing and position estimates are done in the cloud,i.e., outside of the EU and/or eNodeB. There are several options interms of how this can be done. In the case of uplink locate, therelevant UE reference signals transmissions can becollected/pre-processed by the eNodeB and forwarded to the Locate ServerUnit (LSU) for further processing and UE position determination. In caseof the downlink (OTDOA), the task of collecting and pre-processing ofthe downlink reference signals may be performed by the UE. The UE maythen send collected downlink data to the LSU. In case of the downlink(OTDOA), the UE may also handle the communication with the LSU, usingthe Control Plane and/or the LTE User Plane. Consequently, signaling maybe in line with OMA Secure User Plane Location (SUPL) protocol and/or3GPP, for example LTE Positioning Protocol (LPP). In case of the uplinkAoA/DoA the eNodeB may handle the communication with the LSU, using theLPPa and SLmAP (SLm Application Protocol) protocols.

As described in embodiments, this network-centric architecture mayenable advanced features that were not feasible before. These include:(a) utilizing the downlink OTDOA to determine the distance between theserving cell/tower and the UE and uplink AoA/DoA to determine the UElocation, while also mitigating the OTDOA synchronization error impacton this uplink/downlink UE positioning by utilizing trackingalgorithms/technology; (b) utilizing tracking algorithms/technology toimprove the UE positioning in cases where the uplink AoA/DoA plus RTTmethod is used to determine the UE position; and (c) utilizing trackingalgorithms/technology to estimate and to correct/mitigate thesynchronization error in the downlink OTDOA locating method.

As described in embodiments, downlink (OTDOA) UE locate methods includea navigation processor utilizing the multilateration technique/method,aka hyperbolic navigation. This multilateration technique entailssolving a number of hyperbolic (RSTD/TDOA) equations. There areiterative methods and non-iterative (close form) solutions. In oneembodiment, a hybrid approach is described that splits the number ofavailable (hearable) reference points (base stations) to have multiplesets of three RSTD/TDOA subsets and finds closed-form solutions for eachsubset. Thereafter, location consistency algorithms may be utilized tofinalize the position fix. In a second embodiment, the position fix maybe improved utilizing a combination of iterative and non-iterativesolutions from the same set of RSTD/TDOA values. In a third embodiment,initial position estimates may be determined for the UE location, basedon iterative and non-iterative algorithms, by utilizing the uplinkAoA/DoA estimate(s) and the RTT.

As described in embodiments, a wireless network environment sometimesdefies the multilateration method, which requires at least threereference points (for 2-D locate) to obtain the locate fix because onlytwo high power cells are used to flood a large area with RF signals.Accordingly, when RTT is available, the UE is located along an arcdefined by the serving sector azimuth beam width and the RTT/2 range.When AoA/DoA estimates are available, that the UE position may bedetermined at the hyperbola and arc intersection vicinity. Both of thesemethods may also be used. When neither RTT nor AoA/DoA estimates areavailable, the UE location is determined by scoring intersection pointscorresponding to each cell/tower (sector) on a chosen hyperbola 3608.Scoring is based on a difference in angles between the direction a cellis pointing and the direction to a point on the hyperbola and a distancefrom each point to the corresponding cell/tower. Scores may be weightedaccording to their corresponding cell/tower's SNR.

As described in embodiments, the LSU may include a communicationprocessor that is configured for signaling and information exchange withUEs, the eNodeB, and/or network elements. The signaling may be in linewith the OMA SUPL protocol and/or 3GPP LPP/LPPa, or a combination ofLPP, LPPa and SUPL, as well as other protocols that are or may be usedfor communication with the network, for example LCS-AP protocol.

As described in embodiments, the LSU components may be instructionsstored in memory and configured to execute on a processor of a 4.5G MEC(Mobile Edge Computing) server located at a communication network'sedge, The LSU components may be integrated as a hosted app on 4.5G MEC.In a 5G deployment, the LSU components may be hosted in the core networkcomputing cloud. The LSU being hosted in the core network computingcloud may support LaaS data delivery, whereby the UEs act as gateways tothe core network computing cloud and LaaS specifically for protectedphysical location data.

As described in embodiments, the LSU may include a downlink signalprocessor as well as an uplink signal processor and a navigationprocessor.

Additional features and advantages of the embodiments will be set forthin the description that follows, and in part will be apparent from thedescription, or may be learned by practice of the embodiments. Theadvantages of the embodiments will be realized and attained by thestructure particularly pointed out in the written description and claimshereof as well as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the embodiments as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the embodiments and are incorporated in and constitutea part of this specification, illustrate embodiments and together withthe description serve to explain the principles of the embodiments. Inthe drawings:

FIG. 1 and FIG. 1A illustrate narrow bandwidth ranging signal frequencycomponents, in accordance with an embodiment;

FIG. 2 illustrates exemplary wide bandwidth ranging signal frequencycomponents;

FIG. 3A, FIG. 3B and FIG. 3C illustrate block diagrams of master andslave units of an RF mobile tracking and locating system, in accordancewith an embodiment;

FIG. 4 illustrates an embodiment synthesized wideband base band rangingsignal;

FIG. 5 illustrates elimination of signal precursor by cancellation, inaccordance with an embodiment;

FIG. 6 illustrates precursor cancellation with fewer carriers, inaccordance with an embodiment;

FIG. 7 illustrates an embodiment of one-way transfer function phase;

FIG. 8 illustrates an embodiment of a location method;

FIG. 9 illustrates LTE reference signals mapping;

FIG. 10 illustrates an embodiment of an enhanced Cell ID+RTT locatingtechnique;

FIG. 11 illustrates an embodiment of an OTDOA locating technique;

FIG. 12 illustrates the operation of a Time Observation Unit (TMO)installed at an operator's eNB facility, in accordance with anembodiment;

FIG. 13 illustrates an embodiment of a wireless network locate equipmentdiagram;

FIG. 14 illustrates an embodiment of a wireless network locate downlinkecosystem for enterprise applications;

FIG. 15 illustrates an embodiment of a wireless network locate downlinkecosystem for network wide applications;

FIG. 16 illustrates an embodiment of a wireless network locate uplinkecosystem for enterprise applications;

FIG. 17 illustrates an embodiment of a wireless network locate uplinkecosystem for network wide applications;

FIG. 18 illustrates an embodiment of an UL-TDOA environment that mayinclude one or more DAS and/or femto/small cell antennas;

FIG. 19 illustrates an embodiment of an UL-TDOA like that of FIG. 18that may include one or more cell towers that can be used in lieu of DASbase stations and/or femto/small cells;

FIG. 20 illustrates an embodiment of cell level locating;

FIG. 21 illustrates an embodiment of serving cell and sector IDlocating;

FIG. 22 illustrates an embodiment of E-CID plus AoA locating;

FIG. 23 illustrates an embodiment of AoA locating;

FIG. 24 illustrates an embodiment of TDOA with wide and close distancesbetween receiving antenna;

FIG. 25 illustrates an embodiment of a three sector deployment;

FIG. 26 illustrates an embodiment of antenna ports mapping;

FIG. 27 illustrates an embodiment of an LTE Release 11 U-TDOA locatingtechnique;

FIG. 28 illustrates an embodiment of a multichannel Location ManagementUnit (LMU) high level block diagram;

FIG. 29 illustrates an embodiment of a DL-OTDOA technique inwireless/cellular network with a location Server;

FIG. 30 illustrates an embodiment of a U-TDOA technique inwireless/cellular network with a location Server;

FIG. 31 illustrates an embodiment of a depiction of a rackmountenclosure;

FIG. 32 illustrates an embodiment of a high level block diagram ofmultiple single channel LMUs clustered (integrated) in a rackmountenclosure;

FIG. 33 illustrates an embodiment of a high level block diagram ofmultiple small cells with integrated LMU clustered (integrated) in arackmount enclosure (one-to-one antenna connection/mapping); and

FIG. 34 illustrates an embodiment of a high level block diagram of LMUsand DAS integration;

FIG. 35 illustrates an embodiment of a high level block diagram of LMUsand WiFi infrastructure integration.

FIG. 36 illustrates a method for determining a UE location fix in a twotower/cell environment that involves plotting TDOA hyperbolas for thetwo cell/tower sectors.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Reference will now be made in detail to the preferred embodiments of thepresent embodiments, examples of which are illustrated in theaccompanying drawings.

The present embodiments relate to a method and system for RF-basedidentification, tracking and locating of objects, including RTLS.According to an embodiment, the method and system employs a narrowbandwidth ranging signal. The embodiment operates in VHF band, but canbe also used in HF, LF and VLF bands as well as UHF band and higherfrequencies. It employs multi-path mitigation processor. Employingmulti-path mitigation processor increases the accuracy of tracking andlocating implemented by a system.

The embodiment includes small, highly portable base units that allowusers to track, locate and monitor multiple persons and objects. Eachunit has its own ID. Each unit broadcasts an RF signal with its ID, andeach unit is able to send back a return signal, which can include its IDas well as voice, data and additional information. Each unit processesthe returned signals from the other units and, depending on thetriangulation or trilateration and/or other methods used, continuouslydetermines their relative and/or actual locations. The preferredembodiment can also be easily integrated with products such as GPSdevices, smart phones, two-way radios and PDAs. The resulting productwill have all of the functions of the stand-alone devices whileleveraging the existing display, sensors (such as altimeters, GPS,accelerometers and compasses) and processing capacity of its host. Forexample, a GPS device with the device technology describe herein will beable to provide the user's location on a map as well as to map thelocations of the other members of the group.

The size of the preferred embodiment based on an FPGA implementation isbetween approximately 2×4×1 inches and 2×2×0.5 inches, or smaller, asintegrated circuit technology improves. Depending on the frequency used,the antenna will be either integrated into the device or protrudethrough the device enclosure. An ASIC (Application Specific IntegratedCircuit) based version of the device will be able to incorporate thefunctions of the FPGA and most of the other electronic components in theunit or Tag. The ASIC-based stand-alone version of the product willresult in the device size of 1×0.5×0.5 inches or smaller. The antennasize will be determined by the frequency used and part of the antennacan be integrated into the enclosure. The ASIC based embodiment isdesigned to be integrated into products can consist of nothing more thana chipset. There should not be any substantial physical size differencebetween the Master or Tag units.

The devices can use standard system components (off-the-shelfcomponents) operating at multiple frequency ranges (bands) forprocessing of multi-path mitigation algorithms. The software for digitalsignal processing and software-defined radio can be used. The signalprocessing software combined with minimal hardware, allows assemblingthe radios that have transmitted and received waveforms defined by thesoftware.

U.S. Pat. No. 7,561,048 discloses a narrow-bandwidth ranging signalsystem, whereby the narrow-bandwidth ranging signal is designed to fitinto a low-bandwidth channel, for example using voice channels that areonly several kilohertz wide (though some of low-bandwidth channels mayextend into a few tens of kilohertz). This is in contrast toconventional location-finding systems that use channels from hundreds ofkilohertz to tens of megahertz wide.

The advantage of this narrow-bandwidth ranging signal system is asfollows: 1) at lower operating frequencies/bands, conventionallocation-finding systems ranging signal bandwidth exceeds the carrier(operating) frequency value. Thus, such systems cannot be deployed atLF/VLF and other lower frequencies bands, including HF. Unlikeconventional location-finding systems, the narrow-bandwidth rangingsignal system described in U.S. Pat. No. 7,561,048 can be successfullydeployed on LF, VLF and other bands because its ranging signal bandwidthis far below the carrier frequency value; 2) at lower end of RF spectrum(some VLF, LF, HF and VHF bands), e.g., up to UHF band, conventionallocation-finding systems cannot be used because the FCC severely limitsthe allowable channel bandwidth (12-25 kHz), which makes it impossibleto use conventional ranging signals. Unlike conventionallocation-finding systems, the narrow-bandwidth ranging signal system'sranging signal bandwidth is fully compliant with FCC regulations andother international spectrum regulatory bodies; and 3) it is well known(see MRI: the basics, by Ray H. Hashemi, William G. Bradley . . . 2003)that independently of operating frequency/band, a narrow-bandwidthsignal has inherently higher SNR (Signal-to-Noise-Ratio) as compared toa wide-bandwidth signal. This increases the operating range of thenarrow-bandwidth ranging signal location-finding system independently ofthe frequency/band it operates, including UHF band.

Thus, unlike conventional location-finding systems, the narrow-bandwidthranging signal location-finding system can be deployed on lower end ofthe RF spectrum for example VHF and lower frequencies bands, down toLF/VLF bands, where the multipath phenomena is less pronounced. At thesame time, the narrow-bandwidth ranging location-finding system can bealso deployed on UHF band and beyond, improving the ranging signal SNRand, as a result, increasing the location-finding system operatingrange.

To minimize multipath, e.g., RF energy reflections, it is desirable tooperate on VLF/LF bands. However, at these frequencies the efficiency ofa portable/mobile antenna is very small (about 0.1% or less because ofsmall antenna length (size) relative to the RF wave length). Inaddition, at these low frequencies the noise level from natural andmanmade sources is much higher than on higher frequencies/bands, forexample VHF. Together, these two phenomena may limit the applicabilityof location-finding system, e.g. its operating range and/ormobility/portability. Therefore, for certain applications whereoperating range and/or mobility/portability are very important a higherRF frequencies/bands may be used, for example HF, VHF, UHF and UWB.

At VHF and UHF bands, the noise level from natural and manmade sourcesis significantly lower compared to VLF, LF and HF bands; and at VHF andHF frequencies the multi-path phenomena (e.g., RF energy reflections) isless severe than at UHF and higher frequencies. Also, at VHF, theantenna efficiency is significantly better, than on HF and lowerfrequencies, and at VHF the RF penetration capabilities are much betterthan at UHF. Thus, the VHF band provides a good compromise formobile/portable applications. On the other hand in some special cases,for example GPS where VHF frequencies (or lower frequencies) cannotpenetrate the ionosphere (or get deflected/refracted), the UHF can be agood choice. However, in any case (and all cases/applications) thenarrow-bandwidth ranging signal system will have advantages over theconventional wide-bandwidth ranging signal location-finding systems.

The actual application(s) will determine the exact technicalspecifications (such as power, emissions, bandwidth and operatingfrequencies/band). Narrow bandwidth ranging allows the user to eitherreceive licenses or receive exemption from licenses, or use unlicensedbands as set forth in the FCC because narrow band ranging allows foroperation on many different bandwidths/frequencies, including the moststringent narrow bandwidths: 6.25 kHz, 11.25 kHz, 12.5 kHz, 25 kHz and50 kHz set forth in the FCC and comply with the corresponding technicalrequirements for the appropriate sections. As a result, multiple FCCsections and exemptions within such sections will be applicable. Theprimary FCC Regulations that are applicable are: 47 CFR Part 90—PrivateLand Mobile Radio Services, 47 CFR Part 94 personal Radio Services, 47CFR Part 15 Radio Frequency Devices. (By comparison, a wideband signalin this context is from several hundred KHz up to 10-20 MHz.)

Typically, for Part 90 and Part 94, VHF implementations allow the userto operate the device up to 100 mW under certain exemptions (Low PowerRadio Service being an example). For certain applications the allowabletransmitted power at VHF band is between 2 and 5 Watts. For 900 MHz (UHFband) it is 1 W. On 160 kHz 190 kHz frequencies (LF band) the allowabletransmitted power is 1 Watt.

Narrow band ranging can comply with many if not all of the differentspectrum allowances and allows for accurate ranging while stillcomplying with the most stringent regulatory requirements. This holdstrue not just for the FCC, but for other international organizationsthat regulate the use of spectrum throughout the world, includingEurope, Japan and Korea.

The following is a list of the common frequencies used, with typicalpower usage and the distance the tag can communicate with another readerin a real world environment (see Indoor Propagation and Wavelength DanDobkin, W J Communications, V 1.4 7/10/02):

915 MHz 100 mW 150 feet 2.4 GHz 100 mW 100 feet 5.6 Ghz 100 mW  75 feet

The proposed system works at VHF frequencies and employs a proprietarymethod for sending and processing the RF signals. More specifically, ituses DSP techniques and software-defined radio (SDR) to overcome thelimitations of the narrow bandwidth requirements at VHF frequencies.

Operating at lower (VHF) frequencies reduces scatter and provides muchbetter wall penetration. The net result is a roughly ten-fold increasein range over commonly used frequencies. Compare, for example, themeasured range of a prototype to that of the RFID technologies listedabove:

216 MHz 100 mw 700 feet

Utilizing narrow band ranging techniques, the range of commonly usedfrequencies, with typical power usage and the distance the tagcommunication range will be able to communicate with another reader in areal world environment would increase significantly:

From: To: 915 MHz 100 mW 150 feet 500 feet 2.4 GHz 100 mW 100 feet 450feet 5.6 Ghz 100 mW  75 feet 400 feet

Battery consumption is a function of design, transmitted power and theduty cycle of the device, e.g., the time interval between twoconsecutive distance (location) measurements. In many applications theduty cycle is large, 10× to 1000×. In applications with large dutycycle, for example 100×, an FPGA version that transmits 100 mW of powerwill have an up time of approximately three weeks. An ASIC based versionis expected to increase the up time by 10×. Also, ASICs have inherentlylower noise level. Thus, the ASIC-based version may also increase theoperating range by about 40%.

Those skilled in the art will appreciate that the embodiment does notcompromise the system long operating range while significantly increasesthe location-finding accuracy in RF challenging environments (such as,for example, buildings, urban corridors, etc.)

Typically, tracking and location systems employ Track-Locate-Navigatemethods. These methods include Time-Of-Arrival (TOA),Differential-Time-Of-Arrival (DTOA) and combination of TOA and DTOA.Time-Of-Arrival (TOA) as the distance measurement technique is generallydescribed in U.S. Pat. No. 5,525,967. A TOA/DTOA-based system measuresthe RF ranging signal Direct-Line-Of-Site (DLOS) time-of-flight, e.g.,time-delay, which is then converted to a distance range.

In case of RF reflections (e.g., multi-path), multiple copies of the RFranging signal with various delay times are superimposed onto the DLOSRF ranging signal. A track-locate system that uses a narrow bandwidthranging signal cannot differentiate between the DLOS signal andreflected signals without multi-path mitigation. As a result, thesereflected signals induce an error in the estimated ranging signal DLOStime-of-flight, which, in turn, impacts the range estimating accuracy.

The embodiment advantageously uses the multi-path mitigation processorto separate the DLOS signal and reflected signals. Thus, the embodimentsignificantly lowers the error in the estimated ranging signal DLOStime-of-flight. The proposed multi-path mitigation method can be used onall RF bands. It can also be used with wide bandwidth ranging signallocation-finding systems. And it can support variousmodulation/demodulation techniques, including Spread Spectrumtechniques, such as DSS (Direct Spread Spectrum) and FH (FrequencyHopping).

Additionally, noise reduction methods can be applied in order to furtherimprove the method's accuracy. These noise reduction methods caninclude, but are not limited to, coherent summing, non-coherent summing,Matched filtering, temporal diversity techniques, etc. The remnants ofthe multi-path interference error can be further reduced by applying thepost-processing techniques, such as, maximum likelihood estimation(e.g., Viterbi Algorithm), minimal variance estimation (e.g., KalmanFilter), etc.

The embodiment can be used in systems with simplex, half-duplex and fullduplex modes of operation. Full-duplex operation is very demanding interms of complexity, cost and logistics on the RF transceiver, whichlimits the system operating range in portable/mobile deviceimplementations. In a half-duplex mode of operation the reader (oftenreferred to as the “master”) and the tags (sometimes also referred to as“slaves” or “targets”) are controlled by a protocol that only allows themaster or the slave to transmit at any given time.

The alternation of sending and receiving allows a single frequency to beused in distance measurement. Such an arrangement reduces the costs andcomplexity of the system in comparison with full duplex systems. Thesimplex mode of operation is conceptually simpler, but requires a morerigorous synchronization of events between master and target unit(s),including the start of the ranging signal sequence.

In present embodiments the narrow bandwidth ranging signal multi-pathmitigation processor does not increase the ranging signal bandwidth. Ituses different frequency components, advantageously, to allowpropagation of a narrow bandwidth ranging signal. Further ranging signalprocessing can be carried out in the frequency domain by way ofemploying super resolution spectrum estimation algorithms (MUSIC,rootMUSIC, ESPRIT) and/or statistical algorithms like RELAX, or intime-domain by assembling a synthetic ranging signal with a relativelylarge bandwidth and applying a further processing to this signal. Thedifferent frequency component of narrow bandwidth ranging signal can bepseudo randomly selected, it can also be contiguous or spaced apart infrequency, and it can have uniform and/or non-uniform spacing infrequency.

The embodiment expands multipath mitigation technology. The signal modelfor the narrowband ranging is a complex exponential (as introducedelsewhere in this document) whose frequency is directly proportional tothe delay defined by the range plus similar terms whose delay is definedby the time delay related to the multipath. The model is independent ofthe actual implementation of the signal structure, e.g., steppedfrequency, Linear Frequency Modulation, etc.

The frequency separation between the direct path and multipath isnominally extremely small and normal frequency domain processing is notsufficient to estimate the direct path range. For example, a steppedfrequency ranging signal at a 100 KHz stepping rate over 5 MHz at arange of 30 meters (100.07 nanoseconds delay) results in a frequency of0.062875 radians/sec. A multipath reflection with a path length of 35meters would result in a frequency of 0.073355. The separation is0.0104792. Frequency resolution of the 50 sample observable has a nativefrequency resolution of 0.12566 Hz. Consequently, it is not possible touse conventional frequency estimation techniques for the separation ofthe direct path from the reflected path and accurately estimate thedirect path range.

To overcome this limitation the embodiments use a unique combination ofimplementations of subspace decomposition high resolution spectralestimation methodologies and multimodal cluster analysis. The subspacedecomposition technology relies on breaking the estimated covariancematrix of the observed data into two orthogonal subspaces, the noisesubspace and the signal subspace. The theory behind the subspacedecomposition methodology is that the projection of the observable ontothe noise subspace consists of only the noise and the projection of theobservable onto the signal subspace consists of only the signal.

The super resolution spectrum estimation algorithms and RELAX algorithmare capable of distinguishing closely placed frequencies (sinusoids) inspectrum in presence of noise. The frequencies do not have to beharmonically related and, unlike the Digital Fourier Transform (DFT),the signal model does not introduce any artificial periodicity. For agiven bandwidth, these algorithms provide significantly higherresolution than Fourier Transform. Thus, the Direct Line Of Sight (DLOS)can be reliably distinguished from other multi-paths (MP) with highaccuracy. Similarly, applying a threshold method, which will beexplained below, to the artificially produced synthetic wider bandwidthranging signal makes it possible to reliably distinguish DLOS from otherpaths with high accuracy.

In accordance with the embodiment, the Digital signal processing (DSP),can be employed by the multi-path mitigation processor to reliablydistinguish the DLOS from other MP paths. A variety of super-resolutionalgorithms/techniques exist in the spectral analysis (spectrumestimation) technology. Examples include subspace based methods:MUltiple SIgnal Characterization (MUSIC) algorithm or root-MUSICalgorithm, Estimation of Signal Parameters via Rotational InvarianceTechniques (ESPRIT) algorithm, Pisarenko Harmonic Decomposition (PHD)algorithm, RELAX algorithm, etc.

The noted super-resolution algorithms work on the premise that thesignals impinging on the antennas are not fully correlated. Thus, theperformance degrades severely in a highly correlated signal environmentas may be encountered in multipath propagation. Multipath mitigationtechniques may involve a preprocessing scheme called spatial smoothing.As a result, the multipath mitigation process may become computationallyintensive, complicated, i.e., increases the complexity of the systemimplementation. Multipath mitigation with lower system computationalcosts and implementation complexity may be achieved by using thesuper-resolution Matrix Pencil (MP) algorithm. The MP algorithm isclassified as a non-search procedure. Therefore, it is computationallyless complicated and eliminates problems encountered in searchprocedures used in other super-resolution algorithms. Moreover, the MPalgorithm is not sensitive to correlated signals and only requires asingle channel estimate and can also estimate the delays associated withcoherent multipath components.

In all of the abovementioned super-resolution algorithms the incoming(i.e., received) signal is modeled as a linear combination of complexexponentials and their complex amplitudes of frequencies. In case of amulti-path, the received signal will be as follows:

$\begin{matrix}{{{r(t)} = {\beta \times e^{i\; 2\pi\; f \times t}{\sum\limits_{k = 0}^{k = {L - 1}}{\alpha_{k} \times e^{{- i}\; 2\pi\; f \times \tau_{K}}}}}},} & (1)\end{matrix}$

where β×e^(i2πf×t) is the transmitted signal, f is the operatingfrequency, L is the number of multi-path components, andα_(K)=|α_(K)|×e^(jθ) ^(K) and τ_(K) are the complex attenuation andpropagation delay of the K-th path, respectively. The multi-pathcomponents are indexed so that the propagation delays are considered inascending order. As a result, in this model τ₀ denotes the propagationdelay of the DLOS path. Obviously, the τ₀ value is of the most interest,as it is the smallest value of all τ_(K). The phase θ_(K) is normallyassumed random from one measurement cycle to another with a uniformprobability density function U (0,2π). Thus, we assume that α_(K)=const(i.e., constant value)

Parameters α_(K) and τ_(K) are random time-variant functions reflectingmotions of people and equipment in and around buildings. However, sincethe rate of their variations is very slow as compared to the measurementtime interval, these parameters can be treated as time-invariant randomvariables within a given measurement cycle.

All these parameters are frequency-dependent since they are related toradio signal characteristics, such as, transmission and reflectioncoefficients. However, in the embodiment, the operating frequencychanges very little. Thus, the abovementioned parameters can be assumedfrequency-independent.

Equation (1) can be presented in frequency domain as:

$\begin{matrix}{{{A(f)} = {\sum\limits_{k = 0}^{k = {L - 1}}{\alpha_{k} \times e^{{- {i{({2\pi \times \tau_{K}})}}}f}}}},} & (2)\end{matrix}$where: A) is complex amplitude of the received signal, (2π×τ_(K)) arethe artificial “frequencies” to be estimated by a super-resolutionalgorithm and the operating frequency f is the independent variable;α_(K) is the K-th path amplitude.

In the equation (2) the super-resolution estimation of (2π×τ_(K)) andsubsequently τ_(K) values are based on continuous frequency. Inpractice, there is a finite number of measurements. Thus, the variable fwill not be a continuous variable, but rather a discrete one.Accordingly, the complex amplitude A(f) can be calculated as follows:

$\begin{matrix}{{{\hat{A}( f_{n} )} = {\sum\limits_{k = 0}^{k = {L - 1}}{\alpha_{k} \times e^{{- {i{({2\pi \times \tau_{k}})}}} \times f_{n}}}}},} & (3)\end{matrix}$

where Â(f_(n)) are discrete complex amplitude estimates (i.e.,measurements) at discrete frequencies f_(n).

In equation (3) Â(f_(n)) can be interpreted as an amplitude and a phaseof a sinusoidal signal of frequency f_(n) after it propagates throughthe multi-path channel. Note that all spectrum estimation basedsuper-resolution algorithms require complex input data (i.e. complexamplitude).

In some cases, it is possible to convert real signal data, e.g. Re(Â(f_(n))), into a complex signal (e.g., analytical signal). Forexample, such a conversion can be accomplished by using Hilberttransformation or other methods. However, in case of short distances thevalue τ₀ is very small, which results in very low (2π×τ_(K))“frequencies”.

These low “frequencies” create problems with Hilbert transform (or othermethods) implementations. In addition, if only amplitude values (e.g.,Re(Â(f_(n)))) are to be used, then the number of frequencies to beestimated will include not only the (2π×τ_(K)) “frequencies”, but alsotheir combinations. As a rule, increasing the number of unknownfrequencies impacts the accuracy of the super-resolution algorithms.Thus, reliable and accurate separation of DLOS path from othermulti-path (MP) paths requires complex amplitude estimation.

The following is a description of a method and the multi-path mitigationprocessor operation during the task of obtaining complex amplitudeÂ(f_(n)) in presence of multi-path. Note that, while the description isfocused on the half-duplex mode of operation, it can be easily extendedfor the full-duplex mode. The simplex mode of operation is a subset ofthe half-duplex mode, but would require additional eventssynchronization.

In half-duplex mode of operation the reader (often referred to as the“master”) and the tags (also referred to as “slaves” or “targets”) arecontrolled by a protocol that only allows the master or the slave totransmit at any given time. In this mode of operation, the tags (targetdevices) serve as Transponders. The tags receive the ranging signal froma reader (master device), store it in the memory and then, after certaintime (delay), re-transmit the signal back to the master.

An example of ranging signal is shown in FIG. 1 and FIG. 1A. Theexemplary ranging signal employs different frequency components that arecontiguous. Other waveforms, including pseudo random, spaced infrequency and/or time or orthogonal, etc. can be also used for as longas the ranging signal bandwidth remains narrow. In FIG. 1 the timeduration T_(f) for every frequency component is long enough to obtainthe ranging signal narrow-bandwidth property.

Another variation of a ranging signal with different frequencycomponents is shown on FIG. 2. It includes multiple frequencies (f₁, f₂,f₃, f₄, f_(n)) transmitted over long period of time to make individualfrequencies narrow-band. Such signal is more efficient, but it occupiesin a wide bandwidth and a wide bandwidth ranging signal impacts the SNR,which, in turn, reduces the operating range. Also, such wide bandwidthranging signal will violate FCC requirements on the VHF band or lowerfrequencies bands. However, in certain applications this wide-bandwidthranging signal allows an easier integration into existing signal andtransmission protocols. Also, such a signal decreases the track-locatetime.

These multiple-frequency (f₁, f₂, f₃, f₄, f_(n)) bursts may be alsocontiguous and/or pseudo random, spaced in frequency and/or time ororthogonal, etc.

The narrowband ranging mode will produce the accuracy in the form ofinstantaneous wide band ranging while increasing the range at which thisaccuracy can be realized, compared to wide band ranging. Thisperformance is achieved because at a fixed transmit power, the SNR (inthe appropriate signal bandwidths) at the receiver of the narrow bandranging signal is greater than the SNR at the receiver of a widebandranging signal. The SNR gain is on the order of the ratio of the totalbandwidth of the wideband ranging signal and the bandwidth of eachchannel of the narrow band ranging signal. This provides a goodtrade-off when very rapid ranging is not required, e.g., for stationaryand slow-moving targets, such as a person walking or running.

Master devices and Tag devices are identical and can operate either inMaster or Transponder mode. All devices include data/remote controlcommunication channels. The devices can exchange the information andmaster device(s) can remotely control tag devices. In this exampledepicted in FIG. 1 during an operation of a master (i.e., reader)multi-path mitigation processor originates the ranging signal to tag(s)and, after a certain delay, the master/reader receives the repeatedranging signal from the tag(s).

Thereafter, master's multi-path mitigation processor compares thereceived ranging signal with the one that was originally sent from themaster and determines the Â(f_(n)) estimates in form of an amplitude anda phase for every frequency component f₁. Note that in the equation (3)Â(f_(n)) is defined for one-way ranging signal trip. In the embodimentthe ranging signal makes a round-trip. In other words, it travels bothways: from a master/reader to a target/slave and from the target/slaveback to the master/reader. Thus, this round-trip signal complexamplitude, which is received back by the master, can be calculated asfollows:|Â _(RT)(f _(n))|=|Â _(RT)(f _(n))|² and ∠Â _(RT)(f _(n))=2×(∠Â _(RT)(f_(n)))  (4)

There are many techniques available for estimating the complex amplitudeand phase values, including, for example, matching filtering |Â(f_(n))|and ∠Â(f_(n)). According to the embodiment, a complex amplitudedetermination is based on |Â(f_(n))| values derived from the masterand/or tag receiver RSSI (Received Signal Strength Indicator) values.The phase values ∠Â_(RT)(f_(n)) are obtained by comparing the receivedby a reader/master returned base-band ranging signal phase and theoriginal (i.e., sent by reader/master) base band ranging signal phase.In addition, because master and tag devices have independent clocksystems a detailed explanation of devices operation is augmented byanalysis of the clock accuracy impact on the phase estimation error. Asthe above description shows, the one-way amplitude |Â(f_(n))| values aredirectly obtainable from target/slave device. However, the one-way phase∠Â(f_(n)) values cannot be measured directly.

In the embodiment, the ranging base band signal is the same as the onedepicted in FIG. 1. However, for the sake of simplicity, it is assumedherein that the ranging base band signal consists of only two frequencycomponents each containing multiple periods of cosine or sine waves ofdifferent frequency: F₁ and F₂. Note that F₁=f₁ and F₂=f₂. The number ofperiods in a first frequency component is L and the number of periods ina second frequency component is P. Note that L may or may not be equalto P, because for T_(f)=constant each frequency component can havedifferent number of periods. Also, there is no time gap between eachfrequency component, and both F₁ and F₂ start from the initial phaseequal to zero.

FIGS. 3A, 3B and 3C depict block diagrams of a master or a slave unit(tag) of an RF mobile tracking and locating system. F_(OSC) refers tothe frequency of the device system clock (crystal oscillator 20 in FIG.3A). All frequencies generated within the device are generated from thissystem clock crystal oscillator. The following definitions are used: Mis a master device (unit); AM is a tag (target) device (unit). The tagdevice is operating in the transponder mode and is referred to astransponder (AM) unit.

In the preferred embodiment the device consists of the RF front-end andthe RF back-end, base-band and the multi-path mitigation processor. TheRF back-end, base-band and the multi-path mitigation processor areimplemented in the FPGA 150 (see FIGS. 3B and 3C). The system clockgenerator 20 (see FIG. 3A) oscillates at: F_(OSC)=20 MHz; or

ω_(OSC)=2π×20×10⁶. This is an ideal frequency because in actual devicesthe system clocks frequencies are not always equal to 20 MHz: F_(OSC)^(M)=F_(OSC)γ^(M); F_(OSC) ^(AM)=F_(OSC)γ^(AM).

Note that

${\gamma^{M} = \frac{F_{OSC}^{M}}{F_{OSC}}},{{\gamma^{AM} = \frac{F_{OSC}^{AM}}{F_{OSC}}};{{{and}\mspace{14mu}\beta^{M}} = \frac{1}{\gamma^{M}}}},{\beta^{AM} = \frac{1}{\gamma^{AM}}}$

It should be noted that other than 20 MHz F_(OSC) frequencies can beused without any impact on system performance.

Both units' (master and tag) electronic makeup is identical and thedifferent modes of operations are software programmable. The base bandranging signal is generated in digital format by the master′ FPGA 150,blocks 155-180 (see FIG. 2B). It consists of two frequency componentseach containing multiple periods of cosine or sine waves of differentfrequency. At the beginning, t=0, the FPGA 150 in a master device (FIG.3B) outputs the digital base-band ranging signal to its up-converter 50via I/Q DACs 120 and 125. The FPGA 150 starts with F₁ frequency andafter time T₁ start generating F₂ frequency for time duration of T₂.

Since crystal oscillator's frequency might differ from 20 MHz the actualfrequencies generated by the FPGA will be F₁γ^(M) and F₂γ^(M). Also,time T₁ will be T₁β^(M) and T₂ will be T₂β^(M). IT is also assumed thatT₁, T₂, F₂ are such that F₁γ^(M)*T₁β^(M)=F₁T₁ and F₂γ^(M)*T₂β^(M)=F₂T₂,where both F₁T₁& F₂T₂ are integer numbers. That means that the initialphases of F₁ and F₂ are equal to zero.

Since all frequencies are generated from the system crystal oscillator20 clocks, the master′ base-band I/Q DAC(s) 120 and 125 outputs are asfollows: F₁=γ^(M) 20×10⁶×K_(F) ₁ and F₂=γ^(M) 20×10⁶×K_(F) ₂ , whereK_(F) ₁ and K_(F) ₂ are constant coefficients. Similarly, the outputfrequencies TX_LO and RX_LO from frequency synthesizer 25 (LO signalsfor mixers 50 and 85) can be expressed through constant coefficients.These constant coefficients are the same for the master (M) and thetransponder (AM)—the difference is in the system crystal oscillator 20clock frequency of each device.

The master (M) and the transponder (AM) work in a half-duplex mode.Master's RF front-end up-converts the base-band ranging signal,generated by the multi-path mitigation processor, using quadratureup-converter (i.e., mixer) 50 and transmits this up-converted signal.After the base-band signal is transmitted the master switches from TX toRX mode using RF Front-end TX/RX Switch 15. The transponder receives anddown-converts the received signal back using its RF Front-end mixer 85(producing First IF) and ADC 140 (producing Second IF).

Thereafter, this second IF signal is digitally filtered in theTransponder RF back-end processor using digital filters 190 and furtherdown-converted to the base-band ranging signal using the RF back-endquadrature mixer 200, digital I/Q filters 210 and 230, a digitalquadrature oscillator 220 and a summer 270. This base-band rangingsignal is stored in the transponder's memory 170 using Ram Data BusController 195 and control logic 180.

Subsequently, the transponder switches from RX to TX mode using RFfront-end switch 15 and after certain delay t_(RTX) beginsre-transmitting the stored base-band signal. Note that the delay ismeasured in the AM (transponder) system clock. Thus, t_(RTX)^(AM)=t_(RTX) ^(AM). The master receives the transponder transmissionand down-converts the received signal back to the base-band signal usingits RF back-end quadrature mixer 200, the digital I and Q filters 210and 230, the digital quadrature oscillator 220 (see FIG. 3C).

Thereafter, the master calculates the phase difference between F₁ and F₂in the received (i.e., recovered) base-band signal using multi-pathmitigation processor arctan block 250 and phase compare block 255. Theamplitude values are derived from the RF back-end RSSI block 240.

For improving the estimation accuracy it is always desirable to improvethe SNR of the amplitude estimates from block 240 and phase differenceestimates from block 255. In the preferred embodiment the multi-pathmitigation processor calculates amplitude and phase difference estimatesfor many time instances over the ranging signal frequency componentduration (T_(f)). These values, when averaged, improve SNR. The SNRimprovement can be in an order that is proportional to √{square rootover (N)}, where N is a number of instances when amplitude and phasedifference values were taken (i.e., determined).

Another approach to the SNR improvement is to determine amplitude andphase difference values by applying matching filter techniques over aperiod of time. Yet, another approach would be to estimate the phase andthe amplitude of the received (i.e., repeated) base band ranging signalfrequency components by sampling them and integrating over periodT≤T_(f) against the original (i.e., sent by the master/reader) base-bandranging signal frequency components in the I/Q form. The integration hasthe effect of averaging of multiple instances of the amplitude and thephase in the I/Q format. Thereafter, the phase and the amplitude valuescan be translated from the I/Q format to the |Â(f_(n))| and ∠Â(f_(n))format.

Let's assume that at t=0 under master′ multi-path processor control themaster base-band processor (both in FPGA 150) start the base-bandranging sequence.φ_(FPGA) ^(M)(t)=γ^(M)×ω_(OSC)×(K _(F) ₁ (t)),t<T ₁β^(M) ,t<T ₁β^(M);φ_(FPGA) ^(M)(t)=γ^(M)×ω_(OSC)×(K _(F) ₁ (T ₁β^(M))+K _(F) ₂ (t−T₁β^(M))),t>T ₁β^(M),where T_(f)≥T₁β^(M).The phase at master's DAC(s) 120 and 125 outputs are as follows:φ_(DAC) ^(M)(t)=γ^(M)×ω_(OSC)×(K _(F) ₁ (t−t _(DAC) ^(M)))+φ_(DAC)^(M)(0),t<t ₁β^(M+t) _(DAC) ^(M);φ_(DAC) ^(M)(t)=γ^(M)×ω_(OSC)×(K _(F) ₁ (T ₁β^(M))+K _(F) ₂ (t−T ₁β^(M)−t _(DAC) ^(M)))+φ_(DAC) ^(M)))+φ_(DAC) ^(M)(0),t>T ₁β^(M) +t _(DAC)^(M)Note that DACs 120 and 125 have internal propagation delay, t_(DAC)^(M), that does not depend upon the system clock.

Similarly, the transmitter circuitry components 15, 30, 40 and 50 willintroduce additional delay, t_(TX) ^(M), that does not depend upon thesystem clock.

As a result, the phase of the transmitted RF signal by the master can becalculated as follows:φ_(RF) ^(M)(t)=γ^(M)×ω_(OSC)×(K _(F) ₁ (t−t _(DAC) ^(M) −t _(TX) ^(M))+K_(SYN_TX)(t−t _(TX) ^(M)))+φ_(DAC) ^(M)(0)+φ_(SYN_TX) ^(M)(0), t<T₁β^(M) +t _(DAC) ^(M) +t _(TX) ^(M);φ_(RF) ^(M)(t)=γ^(M)×ω_(OSC)×(K _(F) ₁ (T ₁β^(M))+K _(F) ₂ (t−T _(1β)^(M) −t _(DAC) −t _(TX) ^(M))+K _(SYN_TX)(t−t _(TX) ^(M)))+φ_(DAC)^(M)(0)+φ_(SYN_TX) ^(M)(0), t>T ₁β^(M) +t _(DAC) ^(M) +t _(TX) ^(M)

The RF signal from the master (M) experiences a phase shift φ^(MULT)that is a function of the multi-path phenomena between the master andtag.

The φ^(MULT) values depend upon the transmitted frequencies, e.g. F₁ andF₂. The transponder (AM) receiver′ is not able to resolve each pathbecause of limited (i.e., narrow) bandwidth of the RF portion of thereceiver. Thus, after a certain time, for example, 1 microsecond(equivalent to ˜300 meters of flight), when all reflected signals havearrived at the receiver antenna, the following formulas apply:φ_(ANT) ^(M)(t)=γ^(M)×ω_(OSC)×(K _(F) ₁ (t−t _(DAC) ^(M) −t _(TX)^(M))+K _(SYN_TX)(t−t _(TX) ^(M)))+φ_(F) ₁ ^(MULT)+φ_(DAC)^(M)(0)+φ_(SYN_TX) ^(M)(0), 10⁻⁶ t<T ₁β^(M) +t _(DAC) ^(M) +t _(TX)^(M);φ_(ANT) ^(M)(t)=γ^(M)×ω_(OSC)×(K _(F) ₁ (T ₁β^(M))+K _(F) ₂ (t−T _(1β)^(M) −t _(DAC) −t _(TX) ^(M))+K _(SYN_TX)(t−t _(TX) ^(M)))+φ_(F) ₂^(MULT)+φ_(DAC) ^(M)(0)+φ_(SYN_TX) ^(M)(0), t>T ₁β^(M) +t _(DAC) ^(M) +t_(TX) ^(M)+10⁻⁶

In the AM (transponder) receiver at the first down converter, element85, an output, e.g. first IF, the phase of the signal is as follows:

φ_(IF _ 1)^(AM)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(t − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _ 1)t) + φ_(F₁)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0), 10⁻⁶ < t < T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM);φ_(IF _ 1)^(AM)(t) = γ^(M) × ω_(OSC) × (K_(F₁ )(T₁β^(M)) + K_(F₂)(t − T₁β^(M) − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _ 1)t) + φ_(F 2)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0), t > T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + 10⁻⁶

Note that the propagation delay t_(RX) ^(AM) in the receiver RF section(elements 15 and 60-85) does not depend upon the system clock. Afterpassing through RF Front-end filters and amplifiers (elements 95-110 and125) the first IF signal is sampled by the RF Back-end ADC 140. It isassumed that ADC 140 is under-sampling the input signal (e.g., firstIF). Thus, the ADC also acts like a down-converter producing the secondIF. The first IF filters, amplifiers and the ADC add propagation delaytime. At the ADC output (second IF):

φ_(ADC)^(AM)(t) = γ^(M) × ω_(OSC) × (K_(F₁)(t − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _ 1)^(AM) − t_(ADC)^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _ 1)^(AM) − t_(ADC)^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _ 1)(t − t_(IF _ 1)^(AM) − t_(ADC)^(AM)) + K_(ADC)(t)) + φ_(F₁)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0),  10⁻⁶ < t < T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM);φ_(ADC)^(AM)(t) = γ^(M) × ω_(OSC) × (K_(F₁)(T₁β^(M)) + K_(F₂)(t − T₁β^(M) − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _ 1)^(AM) − t_(ADC)^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _ 1)^(AM) − t_(ADC)^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _ 1)(t − t_(IF _ 1)^(AM) − t_(ADC)^(AM)) + K_(ADC)(t)) + φ_(F2)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0),  t > T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + 10⁻⁶

In the FPGA 150 the second IF signal (from the ADC output) is filteredby the RF Back-end digital filters 190 and further down-converted backto base-band ranging signal by the third down-converter (i.e.,quadrature mixer 200, digital filters 230 and 210 and digital quadratureoscillator 220), summed in the summer 270 and is stored in the memory170. At the third down-converter output (i.e., quadrature mixer):

φ_(BB)^(AM)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(t − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _ 1)(t − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM)) + K_(ADC)(t − t_(FIR)β^(AM)) + K_(SYN _ RX _2)t) + φ_(F₁)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0) − φ_(SYN _ RX _2)^(AM)(0), 10⁻⁶ < t < T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + t_(FIR)β^(AM);φ_(BB)^(AM)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(T₁β^(M)) + K_(F₂)(t − T₁β^(M) − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _ 1)(t − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM)) + K_(ADC)(t − t_(FIR)β^(AM)) + K_(SYN _ RX _2)t) + φ_(F₂)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0) − φ_(SYN _ RX _2)^(AM)(0), t > T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + t_(FIR)β^(AM) + 10⁻⁶

Note that propagation delay t=t_(FIR) ^(AM)=t_(FIR) ^(AM) in the FIRsection 190 does not depend upon the system clock.

After RX→TX delay the stored (in memory 170) base-band ranging signalfrom the master (M) is retransmitted. Note that RX→TX delay t_(RTX)^(AM)=t_(RTX)β^(AM).

φ_(RF)^(AM)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(t − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(t − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(ADC)(t − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(SYN _ RX _2)(t − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) − K_(SYN _ TX)(t − t_(TX)^(AM))) + φ_(F₁)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0) − φ_(SYN _ RX _ 2)^(AM)(0) + φ_(SYN _ TX)^(AM)(0), 10⁻⁶ < t < T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + t_(FIR)β^(AM) + t_(RTX)β^(AM) + t_(DAC)^(AM) + t_(TX)^(AM); φ_(RF)^(AM)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(T₁β^(M)) + K_(F₂)(t − T₁β^(M) − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(t − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(ADC)(t − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(SYN _ RX _2)(t − t_(RTX)β^(AM) − t_(TX)^(AM) − t_(TX)^(AM)) − K_(SYN _ TX)(t − t_(TX)^(AM))) + φ_(F₂)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0) − φ_(SYN _ RX _ 2)^(AM)(0) + φ_(SYN _ TX)^(AM)(0), t > T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + t_(FIR)β^(AM) + t_(RTX)β^(AM) + t_(DAC)^(AM) + t_(TX)^(AM) + 10⁻⁶

By the time the signal from the transponder reaches the master′ (M)receiver antenna the RF signal from transponder (AM) experiences anotherphase shift φ^(MULT) that is a function of the multi-path. As discussedabove, this phase shift happens after a certain time period when allreflected signals have arrived at the master′ receiver antenna:

φ_(ANT)^(M)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(t − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(t − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(ADC)(t − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(SYN _ RX _2)(t − t_(RTX)β^(AM) − t_(TX)^(AM) − t_(TX)^(AM)) − K_(SYN _ TX)(t − t_(TX)^(AM))) + 2 × φ_(F₁)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0) − φ_(SYN _ RX _ 2)^(AM)(0) + φ_(SYN _ TX)^(AM)(0), 2 × 10⁻⁶ < t < T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + t_(FIR)β^(AM) + t_(RTX)β^(AM) + t_(DAC)^(AM) + t_(TX)^(AM);φ_(ANT)^(M)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(T₁β^(M)) + K_(F₂)(t − T₁β^(M) − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(t − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(ADC)(t − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM)) + K_(SYN _ RX _2)(t − t_(RTX)β^(AM) − t_(TX)^(AM) − t_(TX)^(AM)) − K_(SYN _ TX)(t − t_(TX)^(AM))) + 2 × φ_(F₂)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0) − φ_(SYN _ RX _ 2)^(AM)(0) + φ_(SYN _ TX)^(AM)(0), t > T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + t_(FIR)β^(AM) + t_(RTX)β^(AM) + t_(DAC)^(AM) + t_(TX)^(AM) + 2 × 10⁻⁶

In the master receiver the signal from transponder goes through the samedown-conversion process as in the transponder receiver. The result isthe recovered base-band ranging signal that was originally sent by themaster.

For the first frequency component F₁:

φ_(BB _ RECOV)^(M)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(t − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ RX _ 1)(t − t_(IF _ 1)^(M) − t_(AD C)^(M) − t_(FIR)β^(M)) − K_(ADC)(t − t_(FIR)β^(M)) − K_(SYN _ RX _2 )(t)) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _ 1)(t − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(AM)) + K_(ADC)(t − t_(FIR)β^(AM) − t_(DAC)^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) + K_(SYN _ RX _ 2)(t − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ TX)(t − t_(TX)^(AM) − t_(RX)^(M) − F_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M))) + 2 × φ_(F₁)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0) − φ_(SYN _ RX _ 2)^(AM)(0) + φ_(SYN _ TX)^(AM) − φ_(SYN _ RX _ 1)^(M)(0) − φ_(ADC _ CLK)^(M)(0) − φ_(SYN _ RX _ 2)^(M)(0), 2 × 10⁻⁶ < t < T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + t_(FIR)β^(AM) + t_(RTX)β^(AM) + t_(DAC)^(AM) + t_(TX)^(AM) + t_(RX)^(M) + t_(IF _1)^(M) + t_(ADC)^(M) + t_(FIR)β^(AM);

For the second frequency component F2:

φ_(BB _ RECOV)^(M)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(T₁β^(M)) + K_(F₂)(t − T₁β^(M) − t_(DAC)^(M) − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) + K_(SYN _ TX)(t − t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ RX _ 1)(t − t_(IF _ 1)^(M) − t_(AD C)^(M) − t_(FIR)β^(M)) − K_(ADC)(t − t_(FIR)β^(M)) − K_(SYN _ RX _2 )(t)) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(t − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(AM)) + K_(ADC)(t − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(AM) − t_(IF _1)^(M) − t_(ADC)^(M)t_(FIR)β^(M)) + K_(SYN _ RX _ 2)(t − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ TX)(t − t_(TX)^(AM) − t_(RX)^(M) − F_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M))) + 2 × φ_(F₂)^(MULT) + φ_(SYN _ TX)^(M)(0) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(AM)(0) − φ_(SYN _ RX _ 2)^(AM)(0) + φ_(SYN _ TX)^(AM) − φ_(SYN _ RX _ 1)^(AM)(0) − φ_(ADC _ CLK)^(M)(0) − φ_(SYN _ RX _ 2)^(M)(0), t > T₁β^(M) + t_(DAC)^(M) + t_(TX)^(M) + t_(RX)^(AM) + t_(IF _1)^(AM) + t_(ADC)^(AM) + t_(FIR)β^(AM) + t_(RTX)β^(AM) + t_(DAC)^(AM) + t_(TX)^(AM) + t_(RX)^(M) + t_(IF _1)^(M) + t_(ADC)^(M) + t_(FIR)β^(AM) + 2 × 10⁻⁶

Substitutions:T _(D_M-AM) =t _(DAC) ^(M) +t _(TX) ^(M) +t _(RX) ^(AM) +t _(IF_1) ^(AM)+t _(ADC) ^(AM) +t _(FIR)β^(AM) +t _(RTX)β^(AM) +t _(DAC) ^(AM) +t _(TX)^(AM) +t _(RX) ^(M) +t _(IF_1) ^(M) +t _(ADC) ^(M) +t _(FIR)β^(M);where T_(D_M-AM) is the propagation delay through master (M) andtransponder (AM) circuitry.φ_(BB_M-AM)(0)=φ_(SYN_TX) ^(M)(0)−φ_(SYN_RX_1) ^(AM)(0)−φ_(ADC_CLK)^(AM)(0)−φ_(SYN_RX_2) ^(AM)(0)+φ_(SYN_RX_1) ^(M)(0)−φ_(ADC_CLK)^(M)(0)−φ_(SYN_RX_2) ^(M)(0)=Const;where: φ_(BB_M-AM)(0) is the LO phase shift, at time t=0, from master(M) and transponder (AM) frequency mixers, including ADC(s).Also: K_(SYN_TX)=K_(SYN_RX_1)K_(ADC)+K_(SYN_RX_2)

First frequency component F1:

φ_(BB _ RECOV)^(M)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(t − T_(D _ M − AM)) − K_(SYN _ TX)(t) + K_(SYN _ RX _1)(t) − K_(A D C)(t) − K_(SYN _ RX _ 2)(t) + K_(SYN _ TX)(−t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ RX _ 1)(−t_(IF _ 1)^(M) − t_(AD C)^(M) − t_(FIR)β^(M)) − K_(ADC)(−t_(FIR)β^(M))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1) + K_(ADC)(t) + K_(SYN _ RX _2)(t) − K_(SYN _ TX)(t) + K_(SYN _ RX _1)(t − t_(IF _ 1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM)) + K_(ADC)(−t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM)) + K_(SYN _ RX _2)(−t_(RTX)β^(AM) − t_(DAC)^(AM))K_(SYN _ RX _ 1)(−t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) + K_(ADC)(−t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) + K_(SYN _ RX _2)(−t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ TX)(−t_(TX )^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M))) + 2 × φ_(F₁)^(MULT) + φ_(BB _ M − AM)(0), 2 × 10⁻⁶ < t < T₁β^(M) + T_(D _ M − AM);

First frequency component F1 continued:

φ_(BB _ RECOV)^(M)(t) = γ^(M) × ω_(OSC) × (K_( F₁)(t − T_(D _ M − AM)) − K_(SYN _ TX)(−t_(TX)^(M) − t_(RX)^(AM) − t_(IF _ 1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ RX _1)(−t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(AM)) − K_(ADC)(−t_(FIR)β^(M))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(−t_(IF _ 1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM)) + K_(ADC)(−t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM)) + K_(SYN _ RX _2)(−t_(RTX)β^(AM) − t_(DAC)^(AM))) + 2 × φ_(F₁)^(MULT) + φ_(BB _ M − AM)(0), 2 × 10⁻⁶ < t < T₁β^(M) + T_(D _ M − AM);

Second frequency component F2:

φ_(BB _ RECOV)^(M)(t) = γ^(M) × ω_(OSC) × (K_(F₁)(T₁β^(M)) + K_(F₂)(t − T₁β^(M) − T_(D _ M − AM)) − K_(SYN _ TX)(t) + K_(SYN _ RX _1) − K_(ADC)(t) − K_(SYN _ RX _2)(t) + K_(SYN _ TX)(−t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ RX _1)(−t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(ADC)(−t_(FIR)β^(M))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(t) + K_(ADC)(t) + K_(SYN _ RX _2)(t) − K_(SYN _ TX)(t) + K_(SYN _ RX _1)(−t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM)) + K_(ADC)(−t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(ADC)^(AM)) + K_(SYN _ RX _2)(−t_(RTX)β^(AM) − t_(DAC)^(AM))K_(SYN _ RX _1)(−t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) + K_(ADC)(−t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) + K_(SYN _ RX _ 2)(−t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ TX)(−t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M))) + 2 × φ_(F₂)^(MULT) + φ_(BB _ M − AM)(0), t > T₁β^(M) + T_(D _ M − AM) + 2 × 10⁻⁶

Second frequency component F2, continued:

φ_(BB _ RECOV)^(M)(t) = γ^(M) × ω_(OSC) × (K_(F₁)(T₁β^(M)) + K_(F₂)(t − T₁β^(M) − T_(D _ M − AM)) + K_(SYN _ TX)(−t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ RX _1)(−t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(ADC)(−t_(FIR)β^(M))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(−t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM)) + K_(ADC)(−t_(FIR)β^(M) − t_(RTX)β^(AM) − t_(DAC)^(AM)) + K_(SYN _ RX _ 2)(−t_(RTX)β^(AM) − t_(DAC)^(AM))) + 2 × φ_(F₂)^(MULT) + φ_(BB _ M − AM)(0), t > T₁β^(M) + T_(D _ M − AM) + 2 × 10⁻⁶

Further substituting:

α = γ^(M) × ω_(OSC) × (K_(SYN _ TX)(−t_(TX)^(M) − t_(RX)^(AM) − t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(DAC)^(AM) − t_(TX)^(AM) − t_(RX)^(M) − t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(SYN _ RX _1)(−t_(IF _1)^(M) − t_(ADC)^(M) − t_(FIR)β^(M)) − K_(ADC)(−t_(FIR)β^(M))) − γ^(AM) × ω_(OSC) × (K_(SYN _ RX _1)(−t_(IF _1)^(AM) − t_(ADC)^(AM) − t_(FIR)β^(M) − t_(RTX)β^(AM)) + K_(ADC)(−t_(FIR)β^(AM) − t_(RTX)β^(AM) − t_(ADC)^(AM)) + K_(SYN _ RX _ 2)(−t_(RTX)β^(M) − t_(DAC)^(AM))),where α is a constant.

Then the final phase equations is:

$\begin{matrix}{{{{\varphi_{{BB}\;\_\;{RECOV}}^{M}(t)} = {{\gamma^{M} \times \omega_{OSC} \times ( {K_{F_{1}}( {t - T_{{D\;\_\; M} - {AM}}} )} )} + {2 \times \varphi_{F_{2}}^{MULT}} + {\varphi_{{{BB}\;\_\; M} - {AMN}}(0)} + \alpha}},{{{2 \times 10^{- 6}} < t < {{T_{1}\beta^{M}} + T_{{D\;\_\; M} - {AM}}}};}}{{{\varphi_{{BB}\;\_\;{RECOV}}^{M}(t)} = {{\gamma^{M} \times \omega_{OSC} \times ( {{K_{F_{1}}( {T_{1}\beta^{M}} )} + {K_{F_{2}}( {t - {T_{1}\beta^{M}} - T_{{D\;\_\; M} - {AM}}} )}} )} + {2 \times \varphi_{F_{2}}^{MULT}} + {\varphi_{{{BB}\;\_\; M} - {AM}}(0)} + \alpha}},{t > {{T_{1}\beta^{M}} + T_{{D\;\_\; M} - {AM}} + {2 \times 10^{- 6}}}}}} & (5)\end{matrix}$

From the equation (5):

${\angle\;{{\hat{A}}_{RT}( f_{n} )}} = \langle \begin{matrix}{{2 \times \varphi_{F_{1}}^{MULT}};{{2 \times \varphi_{F_{1}}^{MULT}} + {2 \times \Delta\;\Phi_{F_{1}/F_{2}}}};{{2 \times \varphi_{F_{1}}^{MULT}} + {2 \times \Delta\;\Phi_{F_{1}/F_{3}}}};} \\{{{2 \times \varphi_{F_{1}}^{MULT}} + {2 \times \Delta\;\Phi_{F_{1}/F_{4}}}};\ldots\mspace{14mu};{{2 \times \varphi_{F_{1}}^{MULT}} + {2 \times \Delta\;\Phi_{F_{1}/F_{2}}}};}\end{matrix} \rangle$where i=2, 3, 4 . . . ; and 2×ΔΦ_(F) ₁ _(/F) ₁ is equal to 2×(φ_(F) ₁^(MULT)−φ_(F) ₁ ^(MULT)).

For example, the difference 2×(φ_(F) ₂ ^(MULT)−φ_(F) ₂ ^(MULT)) at timeinstances t1 and t2:2×φ_(F) ₂ ^(MULT)−2×φ_(F) ₁ ^(MULT)=2×ΔΦ_(F) ₁ _(/F) ₂ =φ_(BB_RECOV)^(M)(t ₂)−φ_(BB_RECOV) ^(M)(t ₁)−γ^(M)×ω_(OSC)×[K _(F) ₁ (T ₁β^(M))+(K_(F) ₂ (t ₂ −T ₁β^(M) −T _(D_M-AM)))−(K _(F) ₁ (t ₁ −T _(D_M-AM)))],2×10⁻⁶ <t ₁ <T ₁β^(M) +T _(D_M-AM) ;t ₂ >T ₁β^(M) +T _(D_M-AM)+2×10⁻⁶

To find 2×ΔΦ_(F) ₁ _(/F) ₂ difference we need to know T_(D_M-AM):T _(D_M-AM) =T _(LB_M)β^(M) +T _(LB_AM)β^(AM) +t _(RTX)β^(AM);T _(LB_M) =t _(DAC) ^(M) +t _(TX) ^(M) +t _(RX) ^(M) +t _(IF_1) ^(M) +t_(ADC) ^(M) +t _(FIR)β^(M) ;T _(LB_AM) =t _(DAC) ^(AM) +t _(TX) ^(AM) t_(RX) ^(AM) +t _(IF_1) ^(AM) +t _(ADC) ^(AM) +t _(FIR)β^(AM),

where T_(LB_M) and T_(LB_AM) are propagation delays through the master(M) and transponder (AM) TX and RX circuitries that are measured byplacing devices in the loop-back mode. Note that the master and thetransponder devices can measure T_(LB_M) and T_(LB_AM) automatically;and we also know the t_(RTX) value.

From the above formulas and t_(RTX) value T_(D_M-AM) can be determinedand consequently, for a given t₁, and t₂ the 2×ΔΦ_(F) ₁ _(/F) ₂ valuecan be found as follows:

$\begin{matrix}{{2 \times \Delta\;\Phi_{F_{1}/F_{2}}} = {{\varphi_{{BB}\;\_\;{RECOV}}^{M}( t_{2} )} - {\varphi_{{BB}\;\_\;{RECOV}}^{M}( t_{1} )} - {\gamma^{M} \times \omega_{OSC} \times {\quad{\lbrack {{K_{F_{1\;}}( {T_{1}\beta^{M}} )} + {K_{F_{2}}t_{2}} - {K_{F_{2}}T_{1}\beta^{M}} - {K_{F\; 1}t_{1}} - {K_{F_{2}}T_{{LB}\;\_\; M}\beta^{M}} + {K_{F_{1}}T_{{LB}\;\_\; M}\beta^{M}} - {K_{F_{2}}( {{T_{{LB}\;\_\;{AM}}\beta^{AM}\beta^{M}} + {t_{RTX}\beta^{M}}} )} + {K_{F_{1\;}}( {{T_{{LB}\;\_\;{AM}}\beta^{AM}\beta^{M}} + {t_{RTX}\beta^{M}}} )}} \rbrack,{{{2 \times 10^{- 6}} < t_{1} < {{T_{1}\beta^{M}} + T_{{D\;\_\; M} - {AM}}}};{t_{2} = {t_{1} + {T_{1}\beta^{M}{{2 \times \Delta\;\Phi_{F_{1}/F_{2}}} = {{\varphi_{{BB}\;\_\;{RECOV}}^{M}( t_{2} )} - {\varphi_{{BB}\;\_\;{RECOV}}^{M}( t_{1} )} - {\gamma^{M} \times \omega_{OSC} \times \lbrack {{K_{F_{2}}t_{2}} - {K_{F\; 1}t_{1}} - {( {K_{F_{2}} - K_{F_{1}}} ) \times T_{1}\beta^{M}} - {( {K_{F_{2}} - K_{F_{1}}} ) \times T_{{LB}\;\_\; M}\beta^{M}} - {( {K_{F_{2\;}} - K_{F_{1\;}}} ) \times ( {{T_{{LB}\;\_\;{AM}}\beta^{AM}\beta^{M}} + {t_{RTX}\beta^{M}}} )}} \rbrack}}}}}}},{{{2 \times 10^{- 6}} < t_{1} < {{T_{1}\beta^{M}} + T_{{D\;\_\; M} - {AM}}}};{t_{2} = {{t_{1} + {T_{1}\beta^{M}2 \times \Delta\;\Phi_{F_{1}/F_{2}}}} = {{\varphi_{{BB}\;\_\;{RECOV}}^{M}( t_{2} )} - {\varphi_{{BB}\;\_\;{RECOV}}^{M}( t_{1} )} - {\gamma^{M} \times \omega_{OSC} \times \lbrack {{K_{F_{2}}t_{2}} - {K_{F\; 1}t_{1}} - {( {K_{F_{2}} - K_{F_{1}}} ) \times ( {{T_{1}\beta^{M}} - {T_{{LB}\;\_\; M}\beta^{M}} - {T_{{LB}\;\_\;{AM}}\beta^{AM}\beta^{M}} + {t_{RTX}\beta^{M}}} )}} \rbrack}}}}},\mspace{20mu}{{{2 \times 10^{- 6}} < t_{1} < {{T_{1}\beta^{M}} + T_{{D\;\_\; M} - {AM}}}};{t_{2} = {t_{1} + {T_{1}\beta^{M}}}};}}}}}} & (6)\end{matrix}$Or, assuming that β^(M)=β^(AM)=1:2×ΔΦ_(F) ₁ _(/F) ₂ =φ_(BB_RECOV) ^(M)(t ₂)−φ_(BB_RECOV) ^(M)(t₁)−γ^(M)×ω_(OSC)×[K _(F) ₂ t ₂ −K _(F1) t ₁−(K _(F) ₂ −K _(F) ₁ )×(T ₁−T _(D_M-AM))],  (6A)2×10⁻⁶ <t ₁ <T ₁ +T _(D_M-AM) ;t ₂ =t ₁ ±T ₁;

From the equation (6) it can be concluded that at operating frequency(s)ranging signal(s) complex amplitude values can be found from processingthe returned base-band ranging signal.

The initial phase value 2×φ_(F) ₁ ^(MULT) can be assumed to be equalzero because the subspace algorithms are not sensitive to a constantphase offset. If necessary, the 2×φ_(F) ₁ ^(MULT) value (phase initialvalue) can be found by determining the TOA (Time Of Arrival) using thenarrow-bandwidth ranging signal method as described in U.S. Pat. No.7,561,048, incorporated herein by reference in its entirety. This methodestimates the ranging signal round trip delay, which is equal to2×T_(FLT)β^(M) and the 2×φ_(F) ₁ ^(MULT) value can be found from thefollowing equation:2×φ_(F) ₁ ^(MULT)=2×β^(M)×γ^(M)×ω_(OSC)(K _(SYN_TX) +K _(F) ₁ )×(T_(FLT)),Or:2×φ_(F) ₁ ^(MULT)=2×ω_(OSC)×(K _(SYN_TX) +K _(F) ₁ )×(T _(FLT)),

In the preferred embodiment, the returned base-band ranging signal phasevalues φ_(BB_RECOV) ^(M)(t) are calculated by the multi-path processor'sarctan block 250. To improve SNR, the multi-path mitigation processorphase compare block 255 calculates 2×ΔΦ_(F) ₁ _(/F) ₁ φ_(BB_RECOV)^(M)(t_(m))−φ_(BB_RECOV) ^(M)(t_(n)) for many instances n (n=2, 3, 4 . .. ) using the equation (6A), and then average them out to improve SNR.Note that 2×10⁻⁶<t_(n)<T_(f)+T_(D_M-AM); t_(m)=t₁+T_(f).

From the equations 5 and 6 it becomes apparent that the recovered (i.e.,received) base-band ranging signal has the same frequency as theoriginal base-band signal that was sent by the master. Thus, there is nofrequency translation despite the fact that the master (M) and thetransponder (AM) system clocks can differ. Because the base-band signalconsists of several frequency components, each component is consists ofmultiple periods of a sinusoid, it is also possible to estimate thephase and the amplitude of the received ranging signal by sampling thereceived base-band signal individual component frequency with thecorresponding original (i.e., sent by the master) base-band signalindividual frequency component and integrating the resulting signal overperiod T≤T_(f).

This operation generates complex amplitude values Â_(RT) (f_(n)) ofreceived ranging signal in the I/Q format. Note that each base-bandsignal individual frequency component that was sent by the master has tobe shifted in time by the T_(D_M-AM). The integration operation produceseffect of averaging out the multiple instances of the amplitude and thephase (e.g., increasing the SNR). Note that the phase and the amplitudevalues can be translated from the I/Q format to the |Â(f_(n))| and∠Â_(RT)(f_(n)) format.

This method of sampling, integrating over period of T≤T_(f) andsubsequent conversion from the I/Q format to the |Â(f_(n))| and∠Â(f_(n)) format can be implemented in the phase compare block 255 inFIG. 3C. Thus, depending upon the block's 255 design and implementation,either the method of the preferred embodiment, based on the equation(5), or an alternative method, described in this section, can be used.

Although the ranging signal bandwidth is narrow, the frequencydifference f_(n)−f₁ can be relatively large, for example, in an order ofseveral megahertz. As a result, the receiver's bandwidth has to be keptwide enough to pass all of the f₁:f_(n) ranging signal frequenciescomponents. This wide receiver bandwidth impacts the SNR. To reduce thereceiver effective bandwidth and improve the SNR, the received rangingsignal base-band frequency components can be filtered by the RF back-endprocessor in FPGA 150 by the digital narrow bandwidth filters tuned foreach individual frequency component of the received base-band rangingsignal. However, this large number of digital filters (the number offilters equals to the number of individual frequency components, n) putsadditional burden on the FPGA resources, increasing its cost, size andpower consumption.

In the preferred embodiment only two narrow bandwidth digital filtersare used: one filter is always tuned for f₁ frequency component and theother filter can be tuned for all other frequencies components:f₂:f_(n). Multiple instances of ranging signal are sent by the master.Each instance consists of only two frequencies: f₁:f₂; f₁:f₃ . . . ;f₁:f_(i) . . . ; f₁: f_(n). Similar strategies are also possible.

Please note that it is also entirely possible to keep the base-bandranging signal components to only two (or even one) generating the restof the frequency components by adjusting the frequency synthesizers,e.g. changing K_(SYN). It is desirable that LO signals for up-convertersand down-converters mixers are generated using the Direct DigitalSynthesis (DDS) technology. For high VHF band frequencies this canpresent an undesired burden on the transceiver/FPGA hardware. However,for lower frequencies this might be a useful approach. Analog frequencysynthesizers can also be used, but may take additional time to settleafter frequency is changed. Also, in case of analog synthesizers, twomeasurements at the same frequency would have to be made in order tocancel a phase offset that might develop after changing the analogsynthesizer's frequency.

The actual T_(D_M-AM) that is used in the above equations is measured inboth: the master (M) and the transponder (AM) systems clocks, e.g.T_(LB_AM) and t_(RTX) are counted in the transponder (AM) clocks andT_(LB_M) is counted in the master (M) clock. However, when 2×ΔΦ_(F) ₁_(/F) ₂ is calculated both: T_(LB_AM) and t_(RTX) are measured (counted)in master (M) clock. This introduces an error:2×ΔΦ_(ERROR)=γ^(M)×ω_(OSC)×(K _(F) ₂ −K _(F) ₁ )×(T_(LB_AM)(β^(AM)β^(M)−β^(AM))+t _(RTX)(β^(M)−β^(AM)))  (7)

The phase estimation error (7) impacts the accuracy. Therefore, it isnecessary to minimize this error. If β^(M)=β^(AM), in other words, allmaster(s) and transponders (tags) system clocks are synchronized, thenthe contribution from the t_(RTX) time is eliminated.

In the preferred embodiment, the master and the transponder units(devices) are capable of synchronizing clocks with any of the devices.For example, a master device can serve as a reference. Clocksynchronization is accomplished by using the remote controlcommunication channel, whereby under FPGA 150 control, the frequency oftemperature compensated crystal oscillator TCXO 20 is adjusted. Thefrequency difference is measured at the output of the summer 270 of themaster device while the selected transponder device is transmitting acarrier signal.

Thereafter, the master sends a command to the transponder toincrease/decrease TCXO frequency. This procedure may be repeated severaltimes to achieve greater accuracy by minimizing frequency at the summer270 output. Please note that in an ideal case the frequency at thesummer 270 output should become equal to zero. An alternative method isto measure the frequency difference and make a correction of theestimated phase without adjusting the transponder′ TCXO frequency.

While β^(M)−β^(AM) can be considerably reduced there is a phaseestimation error when β^(M)≠1. In this case the margin of error dependsupon a long term stability of the reference device (usually master (M))clock generator. In addition, the process of clock synchronization maytake considerable amount of time, especially with large number of unitsin the field. During the synchronization process the track-locate systembecomes partially or fully inoperable, which negatively impacts thesystem readiness and performance. In this case the abovementioned methodthat does not require the transponder′ TCXO frequency adjustment ispreferred.

Commercially available (off the shelf) TCXO components have high degreeof accuracy and stability. Specifically, TCXO components for the GPScommercial applications are very accurate. With these devices, the phaseerror impact on locating accuracy can be less than one meter without theneed for frequent clock synchronization.

After narrow bandwidth ranging signal multi-path mitigation processorobtains the returned narrow bandwidth ranging signal complex amplitudeÂ_(RT) (f_(n)) the further processing (i.e., execution ofsuper-resolution algorithms), is implemented in the software-basedcomponent, which is a part of the multi-path mitigation processor. Thissoftware component can be implemented in the master (reader) hostcomputer CPU and/or the microprocessor that is embedded in the FPGA 150(not shown). In the preferred embodiment the multi-path mitigationalgorithm(s) software component is executed by the master host computerCPU.

The super-resolution algorithm(s) produce estimation of (2π×τ_(K))“frequencies”, e.g. τ_(K) values. At the final step the multi-pathmitigation processor selects r with the smallest value (i.e., the DLOSdelay time).

In certain cases where the ranging signal narrow bandwidth requirementsare somewhat relaxed, the DLOS path can be separated from MP paths byemploying a continuous (in time) chirp. In the preferred embodiment thiscontinuous chirp is Linear Frequency Modulation (LFM). However, otherchirp waveforms can be also used.

Let's assume that under multi-path mitigation processor control a chirpwith bandwidth of B and duration of T is transmitted. That gives a chirprate of

$\beta = {2\pi\frac{B}{T}}$radians per second. Multiple chirps are transmitted and received back.Note that chirps signals are generated digitally with each chirp startedat the same phase.

In the multi-path processor each received single chirp is aligned sothat the returned chirp is from the middle of the area of interest.

The chirp waveform equation is:s(t)=exp(i(ω₀ t+βt ²)), where ω₀ is the initial frequency for 0<t<T.For a single delay round-trip τ, e.g. no multi-path, the returned signal(cirp) is s(t−τ).

The multi-path mitigation processor then “deramps” the s(t−τ) byperforming complex conjugate mix with the originally transmitted chirp.The resulting signal is a complex sinusoid:f _(τ)(t)=exp(−ω₀τ)exp(−2iβτt)exp(iβτ ²),  (8)where exp(−iw₀τ_(k)) is the amplitude and 2βτ is the frequency and0≤t≤T. Note that the last term is a phase and it is negligible.

In case of multi-path, the composite deramped signal consists ofmultiple complex sinusoids:

$\begin{matrix}{{{f_{MP}(t)} = {\sum\limits_{k = 0}^{k = L}{{\exp( {{- i}\; w_{0}\tau_{k}} )}{\exp( {{- i}\; 2{\beta\tau}_{k}} )}(t)}}},} & (9)\end{matrix}$where L is the number of ranging signal paths, including the DLOS pathand 0≤t≤T.

Multiple chirps are transmitted and processed. Each chirp isindividually treated/processed as described above. Thereafter, themulti-path mitigation processor assembles results of individual chirpsprocessing:

$\begin{matrix}{{f_{MP}^{N}(t)} = {\lbrack {\sum\limits_{n = 0}^{n = {N - 1}}{P( {t - {n\;\rho}} )}} \rbrack \times \lbrack {\sum\limits_{k = 0}^{k = L}{{\exp( {{- {iw}_{0}}\tau_{k}} )}{\exp( {{- i}\; 2\;{\beta\tau}_{k}} )}t}} \rbrack}} & (10)\end{matrix}$where N is the number of chirps,

${{P(t)} = \begin{Bmatrix}{1;{0 \leq t \leq T}} \\{0;{t > T}}\end{Bmatrix}},$ρ=T+t_(dead); t_(dead) is the dead time zone between two consecutivechirps; 2βτ_(k) are artificial delay “frequencies”. Again, the mostinteresting is the lowest “frequency”, which corresponds to the DLOSpath delay.

In the equation (10) f_(MP) ^(N)(t) can be thought of as N samples of asum of complex sinusoids at times:0≤t _(α) ≤T;t ₁ =t _(α) +ρ;t ₂ =t _(α)+2ρ . . . ;t _(m-1) =t_(α)+(N−1)ρ;m∈0:m−1;Thus, the number of samples can be a multiple of N, e.g. αN; α=1, 2, . .. .

From the equation (10) the multi-path mitigation processor produces αNcomplex amplitude samples in time domain that are used in furtherprocessing (i.e., execution of super-resolution algorithms). Thisfurther processing is implemented in the software component, which is apart of the multi-path mitigation processor. This software component canbe executed by the master (reader) host computer CPU and/or by themicroprocessor that is embedded in the FPGA 150 (not shown), or both. Inthe preferred embodiment the multi-path mitigation algorithm(s) softwareis executed by the master host computer CPU.

The super-resolution algorithm(s) produce estimation of 2βτ_(k)“frequencies”, e.g. τ_(K) values. At the final step the multi-pathmitigation processor selects r with the smallest value, i.e. the DLOSdelay time.

An explanation will be given of a special processing method, called the“threshold technique,” which can serve as an alternative to thesuper-resolution algorithms. In other words, it is used to enhancereliability and accuracy in distinguishing DLOS path from other MP pathsusing the artificially generated synthetic wider bandwidth rangingsignal.

The frequency domain base-band ranging signal shown in FIG. 1 and FIG.1A can be converted into time domain base-band signal s(t):

$\begin{matrix}{{s(t)} = \frac{\sin\;{\pi( {{2N} + 1} )}\Delta\; f\; t}{\sin\;{\pi\Delta}\; f\; t}} & (11)\end{matrix}$It is readily verified that s(t) is periodic with period 1/Δt, and forany integer k, that s(k/Δt)=2N+1, which is the peak value of the signal.Where n=N in FIG. 1 and FIG. 1A.

FIG. 4 shows two periods of s(t) for the case where N=11 and Δf=250 kHz.The signal appears as a sequence of pulses of height 2N+1=23 separatedby 1/Δf=4 microseconds. Between the pulses is a sinusoidal waveform withvarying amplitude and 2N zeros. The wide bandwidth of the signal can beattributed to the narrowness of the tall pulses. It can be also seenthat the bandwidth extends from zero frequency to NΔf=2.75 MHz.

The basic idea of the threshold method that is used in the preferredembodiment is to enhance the artificially generated synthetic widerbandwidth ranging reliability and accuracy in distinguishing DLOS pathfrom other MP paths. The threshold method detects when the start of theleading edge of a wideband pulse arrives at a receiver. Because offiltering in the transmitter and receiver, the leading edge does notrise instantaneously, but rises out of the noise with smoothlyincreasing slope. The TOA of the leading edge is measured by detectingwhen the leading edge crosses a predetermined threshold T.

A small threshold is desirable because it gets crossed sooner and theerror delay τ between the true start of the pulse and the thresholdcrossing is small. Thus, any pulse replica arriving due to multi-pathhas no effect if the start of the replica having a delay greater than τ.However, the presence of noise places a limit on how small the thresholdT can be. One way to decrease the delay τ is to use the derivative ofthe received pulse instead of the pulse itself, because the derivativerises faster. The second derivative has an even faster rise. Higherorder derivatives might be used, but in practice they can raise thenoise level to an unacceptable value, so the threshold second derivativeis used.

Although the 2.75 MHz wide signal depicted in FIG. 4 has a fairly widebandwidth, it is not suitable for measuring range by the abovementionedmethod. That method requires transmitted pulses each having azero-signal precursor. However, it is possible to achieve that goal bymodifying the signal so that the sinusoidal waveform between the pulsesis essentially cancelled out. In the preferred embodiment it is done byconstructing a waveform which closely approximates the signal on achosen interval between the tall pulses, and then subtracting it fromthe original signal.

The technique can be illustrated by applying it to the signal in FIG. 1.The two black dots shown on the waveform are the endpoints of aninterval I centered between the first two pulses. The left and rightendpoints of the interval I, which have been experimentally determinedto provide the best results, are respectively at:

$\begin{matrix}{{t_{1} = {\frac{1.1}{( {{2N} + 1} )\Delta\; f} = {\frac{1.1}{23 \times 250,000} \cong {191.3\mspace{14mu}{nsec}}}}}{t_{2} = {{\frac{1}{\Delta\; f} - t_{1}} = {{\frac{1}{250,000} - \frac{1.1}{23 \times 250,000}} \cong {3,808.7\mspace{14mu}{nsec}}}}}} & (12)\end{matrix}$

An attempt to generate a function g(t) which essentially cancels out thesignal s(t) on this interval, but does not cause much harm outside theinterval, is performed. Since the expression (11) indicates that s(t) isthe sinusoid sin π(2N+1)Δft modulated by 1/sin πΔft, first a functionh(t) which closely approximates 1/sin πΔft on the interval I is found,and then form g(t) as the product:g(t)=h(t)sin π(2N+1)Δft  (13)h(t) is generated by the following sum:

$\begin{matrix}{{{h(t)} = {\sum\limits_{k = 0}^{M}{a_{k}{\phi_{k}(t)}{dt}}}},{t \in I}} & (14)\end{matrix}$whereϕ₀(t)≡1,ϕ_(k)(t)=sin kπΔft for k=1,2, . . . ,M  (15)and the coefficients a_(k) are chosen to minimize the least-square error

$\begin{matrix}{J = {\int_{t_{1}}^{t_{2}}{( {{{1/\sin}\;{\pi\Delta}\; f\; t} - {\sum\limits_{k = 0}^{M}{a_{k}{\phi_{k}(t)}}}} )^{2}{dt}}}} & (16)\end{matrix}$over the interval I.

The solution is readily obtained by taking partial derivatives of J withrespect to the a_(k) and setting them equal to zero. The result is thelinear system of M+1 equations

$\begin{matrix}{{{\sum\limits_{k = 0}^{M}{a_{k}R_{jk}}} = R_{j}},{j = 0},1,2,\ldots\mspace{14mu},M} & (17)\end{matrix}$

that can be solved for the a_(k), where

$\begin{matrix}{{R_{j} = {\int_{t_{1}}^{t_{2}}{{\phi_{j} \cdot {1/\sin}}\;{\pi\Delta}\; f\; t\;{dt}}}},{R_{jk} = {\int_{t_{1}}^{t_{2}}{{\phi_{j}(t)}{\phi_{k}(t)}{dt}}}}} & (18)\end{matrix}$

Then,

$\begin{matrix}\begin{matrix}{{g(t)} = {{h(t)}\sin\;{\pi( {{2N} + 1} )}\Delta\; f\; t}} \\{= {( {\sum\limits_{k = 0}^{M}{a_{k}{\phi_{k}(t)}}} )\sin\;{\pi( {{2N} + 1} )}\Delta\; f\; t}}\end{matrix} & (19)\end{matrix}$

Using the definition of the functions ϕ_(k)(t) given by (12)

$\begin{matrix}{{g(t)} = {( {a_{0} + {\sum\limits_{k = 1}^{M}{a_{k}\sin\; k\;{\pi\Delta}\; f\; t}}} )\sin\;{\pi( {{2N} + 1} )}\Delta\; f\; t}} & (20)\end{matrix}$

The g(t) is subtracted from s(t) to get a function r(t), which shouldessentially cancel s(t) on the interval I. An appropriate choice for theupper limit M for the summation in the equation (20) is M=2N+1. Usingthis value,

$\begin{matrix}\begin{matrix}{{r(t)} = {{s(t)} - {g(t)}}} \\{= {b_{0} + {\sum\limits_{k = 1}^{{2N} + 1}{b_{k}\cos\; 2\;\pi\; k\;\Delta\; f\; t}} + {c\;\sin\; 2{\pi( {N + \frac{1}{2}} )}\Delta\; f\; t}}}\end{matrix} & (21)\end{matrix}$

where

$\begin{matrix}{{b_{0} = {1 - {\frac{1}{2}a_{{2N} + 1}}}}{{b_{k} = {{2 - {\frac{1}{2}a_{{2{({N - k})}} + 1}\mspace{14mu}{for}\mspace{14mu} k}} = 1}},2,\ldots\mspace{11mu},N}{{b_{k} = {{{- \frac{1}{2}}a_{{2{({k - N})}} - 1}\mspace{14mu}{for}\mspace{14mu} k} = {N + 1}}},{N + 2},\ldots\mspace{11mu},{{2N} + 1}}{c = {- a_{0}}}} & (22)\end{matrix}$

From the equation (17) it is seen that a total of 2N+3 frequencies(including the zero-frequency DC term) are required to obtain thedesired signal r(t). FIG. 5 shows the resulting signal r(t) for theoriginal signal s(t) shown in FIG. 1, where N=11. In this case theconstruction of r(t) requires 25 carriers (including the DC term b₀).

The important characteristics of r(t) as constructed above are asfollows:

1. The lowest frequency is zero Hz and the highest frequency is (2N+1)ΔfHz, as seen from (14). Thus, the total bandwidth is (2N+1)Δf Hz.

2. All carriers are cosine functions (including DC) spaced Δf apart,except for one carrier, which is a sine function located at frequency(N+½)Δf.

3. Although the original signal s(t) has period 1/Δf, r(t) has period2/Δf. The first half of each period of r(t), which is a full period ofs(t), contains a cancelled portion of the signal, and the secondhalf-period of r(t) is a large oscillatory segment. Thus, cancellationof the precursor occurs only in every other period of s(t).

This occurs because the canceling function g(t) actually strengthenss(t) in every other period of s(t). The reason is that g(t) reverses itspolarity at every peak of s(t), whereas s(t) does not. A method ofmaking every period of s(t) contain a cancelled portion to increaseprocessing gain by 3 dB is described below.

4. The length of the cancelled portion of s(t) is about 80-90% of 1/Δf.Therefore, Δf needs to be small enough to make this length long enoughto eliminate any residual signal from previous non-zero portions of r(t)due to multi-path.

5. Immediately following each zero portion of r(t) is the first cycle ofan oscillatory portion. In the preferred embodiment, in the TOAmeasurement method as described above, the first half of this cycle isused for measuring TOA, specifically the beginning of its rise. It isinteresting to note that the peak value of this first half-cycle (whichwill be called the main peak) is somewhat larger than the correspondingpeak of s(t) located at approximately the same point in time. The widthof the first half-cycle is roughly inversely proportional to NΔf.

6. A large amount of processing gain can be achieved by:

(a) Using the repetitions of the signal r(t), because r(t) is periodicwith period 2/Δf. Also, an additional 3 dB of processing gain ispossible by a method to be described later.

(b) Narrowband filtering. Because each of the 2N+3 carriers is anarrowband signal, the occupied bandwidth of the signal is much smallerthan that of a wideband signal spread out across the entire allocatedband of frequencies.

For the signal r(t) shown in FIG. 5, where N=11 and Δf=250 kHz, thelength of the cancelled portion of s(t) is about 3.7 microseconds or1,110 meters. This is more than enough to eliminate any residual signalfrom previous non-zero portions of r(t) due to the multi-path. The mainpeak has value of approximately 35, and the largest magnitude in theprecursor (i.e., cancellation) region is about 0.02, which is 65 dBbelow the main peak. This is desirable for getting good performanceusing the TOA measurement threshold technique as described above.

Use of fewer carriers is depicted in FIG. 6, which illustrates a signalthat is generated using Δf=850 kHz, N=3, and M=2N+1=7, for a total ofonly 2N+3=9 carriers. In this case, the period of the signal is only2/Δf≈2.35 microseconds as compared to the signal in FIG. 5, where theperiod is 8 microseconds. Since this example has more periods per unittime, one might expect that more processing gain could be achieved.

However, since fewer carriers are used, the amplitude of the main peakis about ⅓ as large as before, which tends to cancel the expected extraprocessing gain. Also, the length of the zero-signal precursor segmentsis shorter, about 0.8 microseconds or 240 meters. This should still beenough to eliminate any residual signal from previous non-zero portionsof r(t) due to the multi-path. Note that the total bandwidth of(2N+1)Δf=5.95 MHz is about the same as before, and that the width of thehalf-cycle of the main peak is also roughly the same. Since fewercarriers are used, there should be some extra processing gain when eachcarrier is narrowband filtered at the receiver. Moreover, the largestmagnitude in the precursor (i.e., cancellation) region is now about 75dB below the main peak, a 10 dB improvement from the previous example.

Transmission at RF Frequencies: up to this point r(t) has been describedas a base-band signal for purposes of simplicity. However, it can betranslated up to RF, transmitted, received, and then reconstituted as abase-band signal at the receiver. To illustrate, consider what happensto one of the frequency components ω_(k) in the base-band signal r(t)traveling via one of the multi-path propagation paths having index j(radian/sec frequencies are used for notational simplicity):b _(k) cos ω_(k) t (at baseband in transmitter)b _(k) cos(ω+ω_(k))t (translated by frequency ω up to RF)a _(j) b _(k) cos [(ω+ω_(k))(t−τ _(j))+ϕ_(j)] (at receiverantenna)  (23)a _(j) b _(k) cos [ω_(k)(t−τ _(j))+ϕ_(j)+θ] (translated by frequency ωto baseband)

It is assumed here that the transmitter and receiver are frequencysynchronized. The parameter b_(k) is the k^(th) coefficient inexpression (21) for r(t). The parameters τ_(j) and ϕ_(j) arerespectively the path delay and phase shift (due to dielectricproperties of a reflector) of the j^(th) propagation path. The parameterθ is the phase shift occurring in the down-conversion to base-band inthe receiver. A similar sequence of functions can be presented for thesine component of the equation (21).

It is important to note that as long as the zero-signal precursors inr(t) have length sufficiently larger than the largest significantpropagation delay, the final base-band signal in the equation (20) willstill have zero-signal precursors. Of course, when all frequencycomponents (index k) over all paths (index j) are combined, thebase-band signal at the receiver will be a distorted version of r(t),including all phase shifts.

Sequential Carrier Transmissions and Signal Reconstruction areillustrated in FIG. 1 and FIG. 1A. It is assumed that the transmitterand the receiver are time and frequency synchronized, the 2N+3transmitted carriers need not be transmitted simultaneously. As anexample, consider the transmission of the signal whose base-bandrepresentation is that of FIG. 1A and FIG. 6.

In FIG. 6, N=3, and suppose each of the 9 frequency components for 1millisecond are sequentially transmitted. The start and the end timesfor each frequency transmission are known at the receiver, so it cansequentially start and end its reception of each frequency component atthose respective times. Since the signal propagation time is very shortcompared to 1 millisecond (it will normally be less than severalmicroseconds in the intended application), a small portion of eachreceived frequency component should be ignored, and the receiver caneasily blank it out.

The entire process of receiving 9 frequency components can be repeatedin 9-millisecond blocks of additional reception to increase theprocessing gain. In one second of total reception time there would beabout 111 such 9-millisecond blocks available for processing gain.Additionally, within each block there would be additional processinggain available from 0.009/(2/Δf)≈383 main peaks.

It is worth noting that in general the signal reconstruction can be madevery economical, and will inherently permit all possible processinggain. For each of the 2N+3 received frequencies:

1. Measure the phase and amplitude of each 1-millisecond reception ofthat frequency to form a sequence of stored vectors (phasors)corresponding to that frequency.

2. Average the stored vectors for that frequency.

3. Finally, use the 2N+3 vector averages for the 2N+3 frequencies toreconstruct 1 period of base-band signal having duration 2/Δf, and usethe reconstruction to estimate signal TOA.

This method is not restricted to 1-millisecond transmissions, and thelength of the transmissions may be increased or decreased. However, thetotal time for all transmissions should be short enough to freeze anymotion of the receiver or transmitter.

Obtaining Cancellation on Alternate Half-Cycles of r(t): by simplyreversing the polarity of the canceling function g(t), cancellationbetween the peaks of s(t) is possible where r(t) was formerlyoscillatory. However, to obtain cancellation between all peaks of s(t),the function g(t) and its polarity reversed version must be applied atthe receiver, and this involves coefficient weighting at the receiver.

Coefficient Weighting at the Receiver: if desired, the coefficientsb_(k) in the equation (21) are used for construction of r(t) at thetransmitter and may be introduced at the receiver instead. This iseasily seen by considering the sequence of signals in the equation (20)in which the final signal is the same if b_(k) is introduced at the laststep instead of at the beginning. Ignoring noise, the values are asfollows:cos ω_(k) t (at baseband in transmitter)cos(ω+ω_(k))t (translated by frequency ω up to RF)a _(j) cos [(ω+ω_(k))(t−τ _(j))+ϕ_(j)] (at receiver antenna)  (24)a _(j) cos [ω_(k)(t−τ _(j))+ϕ_(j)+θ] (translated by frequency −ω tobaseband)a _(j) b _(k) cos [ω_(k)(t−τ _(j))+ϕ_(j)+θ] (weighted by coefficient b_(k) at baseband)

The transmitter can then transmit all frequencies with the sameamplitude, which simplifies its design. It should be noted, that thismethod also weights the noise at each frequency, the effect of whichshould be considered. It should also be noted that coefficient weightingshould be done at the receiver in order to affect the polarity reversalof g(t) to get twice as many useable main peaks.

Scaling of Δf to Center Frequencies in Channels: to meet the FCCrequirements at the VHF or lower frequencies a channelized transmissionwith constant channel spacing will be required. In a channelizedtransmission band with constant channel spacing that is small comparedto the total allocated band, which is the case for the VHF and lowerfrequencies band(s), small adjustments to Δf, if necessary, permit alltransmitted frequencies to be at channel centers without materiallychanging performance from original design values. In the two examples ofbase-band signals previously presented, all frequency components aremultiples of Δf/2, so if the channel spacing divides Δf/2, the lowest RFtransmitted frequency can be centered in one channel and all otherfrequencies fall at the center of channels.

In some Radio Frequency (RF)-based identification, tracking and locatingsystems in addition to performing the distance measurement function,both: the Master Unit and the Tag Unit also perform voice, data andcontrol communication functions. Similarly, in the preferred embodimentboth the Master Unit and the Tag perform voice, data and controlcommunication functions in addition to the distance measurementfunction.

According to the preferred embodiment, the ranging signal(s) are subjectto the extensive sophisticated signal processing techniques, includingthe multi-path mitigation. However, these techniques may not lendthemselves to the voice, data and control signals. As a result, theoperating range of the proposed system (as well as other existingsystems) may be limited not by its ability to measure distance reliablyand accurately, but by being out of range during voice and/or dataand/or control communications.

In other Radio Frequency (RF)-based identification, tracking andlocating systems the distance measurement functionality is separatedfrom the voice, data and control communication functionality. In thesesystems separate RF Transceivers are used to perform voice, data andcontrol communication functions. The drawback of this approach is systemincreased cost, complexity, size, etc.

To avoid abovementioned drawbacks, in the preferred embodiment, a narrowbandwidth ranging signal or base-band narrow bandwidth ranging signalseveral individual frequency components are modulated with the identicaldata/control signals and in case of voice with digitized voice packetsdata. At the receiver the individual frequency components that have thehighest signal strength are demodulated and the obtained informationreliability may be further enhanced by performing “voting” or othersignal processing techniques that utilize the information redundancy.

This method allows to avoid the “null” phenomena, wherein the incomingRF signals from multiple paths are destructively combining with the DLOSpath and each other, thus significantly reducing the received signalstrength and associated with it SNR. Moreover, such method allows tofind a set of frequencies at which the incoming signals from multiplepaths are constructively combining with DLOS path and each other, thusincreasing the received signal strength and associated with it SNR.

As mentioned earlier, spectrum estimation-based super-resolutionalgorithms generally use the same model: a linear combination of complexexponentials and their complex amplitudes of frequencies. This complexamplitude is given by equation 3 above.

All spectrum estimation-based super-resolution algorithms require apriori knowledge of number of complex exponentials, i.e., the number ofmultipath paths. This number of complex exponentials is called the modelsize and is determined by the number of multi-path components L as shownin equations 1-3. However, when estimating path delay, which is the casefor RF track-locate applications, this information is not available.This adds another dimension, i.e., the model size estimation, to thespectrum estimation process via super-resolution algorithms.

It has been shown (Kei Sakaguchi et al., Influence of the Model OrderEstimation Error in the ESPRIT Based High Resolution Techniques) that incase of model size underestimation the accuracy of frequency estimationis impacted and when the model size is overestimated the algorithmgenerates spurious, e.g., non-existent, frequencies. Existing methods ofmodel size estimation such as AIC (Akaikes Information Criterion), MDL(Minimum Description Length), etc. have a high sensitivity tocorrelation between signals (complex exponentials). But in the case ofRF multipath, this is always the case. Even, for example, afterForward-Backward smoothing algorithms are applied, there will always bea residual amount of correlation.

In the Sakaguchi paper, it is suggested to use an overestimated modeland differentiating actual frequencies (signals) from spuriousfrequencies (signals) by estimating these signals power (amplitude) andthen rejecting the signals with very low power. Although this method isan improvement over existing methods, it is not guaranteed. Theinventors implemented the Kei Sakaguchi et al. method and ransimulations for more complex cases with a larger model size. It wasobserved that, in some cases, a spurious signal may have amplitude thatis very close to actual signals amplitude.

All spectrum estimation-based super-resolution algorithms work bysplitting the incoming signal complex amplitude data into twosub-spaces: the noise sub-space and signals sub-space. If thesesub-spaces are properly defined (separated), then the model size isequal to the signal sub-space size (dimension).

In one embodiment, the model size estimation is accomplished using an“F” statistic. For example, for ESPRIT algorithm, the singular valuedecomposition of the estimate of the covariance matrix (withforward/backward correlation smoothing) is ordered in ascending order.Thereafter, a division is made whereby the (n+1) eigenvalue is dividedby the n-th eigenvalue. This ratio is an “F” random variable. The worstcase is an “F” random variable of (1,1) degree of freedom. The 95%confidence interval for a “F” random variable with (1,1) degrees offreedom is 161. Setting that value as a threshold determines the modelsize. Note also that for the noise subspace, the eigenvalues representan estimate of the noise power.

This method of applying “F” statistics to the ratio of the eigenvaluesis a more accurate method of estimating the model size. It should benoted that other degrees of freedom in “F” statistics can be also usedfor threshold calculation and consequently model size estimation.

Nevertheless, in some cases, two or more very closely spaced (in time)signals can degenerate into one signal because of real-world measurementimperfections. As a result, the above mentioned method willunderestimate the number of signals, i.e., the model size. Since modelsize underestimation reduces the frequency estimation accuracy, it isprudent to increase the model size by adding a certain number. Thisnumber can be determined experimentally and/or from simulations.However, when signals are not closely spaced, the model size will beoverestimated.

In such cases spurious, i.e., non-existent, frequencies may appear. Asnoted earlier, using signal amplitude for spurious signals detectiondoes not always work because in some cases a spurious signal(s) wasobserved to have amplitude that is very close to actual signal(s)amplitude. Therefore, in addition to the amplitude discrimination,filters can be implemented to improve spurious frequencies eliminationprobability.

The frequencies that are estimated by super-resolution algorithms areartificial frequencies (equation 2). In fact, these frequencies areindividual paths delays of the multipath environment. As a result, thereshould be no negative frequencies and all negative frequencies that areproduced by a super-resolution algorithm are spurious frequencies to berejected.

Furthermore, a DLOS distance range can be estimated from the complexamplitude Â(f_(n)) values obtained during measurements using methodsthat are different from super-resolution methods. While these methodshave lower accuracy, this approach establishes range that is used todiscriminate delays, i.e., frequencies. For example, the ratio of

$\frac{\Delta\lbrack {\angle\;{\hat{A}( {2{\pi\Delta}\; f} )}} \rbrack}{2{\pi\Delta}\; f}$

in Δf intervals where the signal amplitude |Â(f_(n))| is close tomaximum, i.e., avoiding nulls, provides a DLOS delay range. Althoughactual DLOS delay can be up to two times larger or smaller, this definesa range that helps to reject spurious results.

In the embodiment, the ranging signal makes a round-trip. In otherwords, it travels both ways: from a master/reader to a target/slave andfrom the target/slave back to the master/reader:

Master transmits a tone: α×e^(−jωt), where ω is an operating frequencyin the operating band and α is the tone signal amplitude.

At the target's receiver, the received signal (one-way) is as follows:

$\begin{matrix}{{S_{{one} - {way}}(t)} = {\alpha \times {\sum\limits_{m = 0}^{m = N}{K_{m} \times e^{{- j}\;\omega\; t} \times e^{{- j}\;\omega\;\tau_{m}}}}}} & (25)\end{matrix}$

Where: N is number of signal paths in the multipath environment; K0 andτ₀ are amplitude and time-of-flight of the DLOS signal; |K₀|=1, K₀>0,|K_(m≠0)|≤1 and K_(m≠0) can be positive or negative.S _(one-way)(t)=α×e ^(−jωt) ×A(ω)×e ^(−jθ(ω))  (26)

Where:

${{A(\omega)} \times e^{{- j}\;{\theta{(\omega)}}}} = {\sum\limits_{m = 0}^{m = N}{K_{m} \times e^{{- j}\;\omega\;\tau_{m}}}}$is one way multipath RF channel transfer function in the frequencydomain; and A(ω)≥0.

Target retransmits the received signal:S _(retransmit)(t)=α×e ^(−jωt) ×A(ω)×e ^(−jθ(ω))  (27)

At the master receiver, the round-trip signal is:

${S_{{round}\;\_\;{trip}}(t)} = {\alpha \times e^{{- j}\;\omega\; t} \times {A(\omega)} \times e^{{- j}\;{\theta{(\omega)}}} \times {\sum\limits_{m = 0}^{m = N}{K_{m} \times e^{{- j}\;\omega\;\tau_{m}}}}}$

Or:S _(round_trip)(t)=α×e ^(−jωt) ×A ²(ω)×e ^(−j2θ(ω))  (28)

On the other hand from equations (26) and (28):

$\begin{matrix}{{S_{{round}\;\_\;{trip}}(t)} = {\alpha \times e^{{- j}\;\omega\; t} \times {A^{2}(\omega)} \times ( {\sum\limits_{m = 0}^{m = N}{K_{m} \times e^{{- j}\;\omega\;\tau_{m}}}} )^{2}}} & (29)\end{matrix}$

Where:

${{A^{2}(\omega)} \times ( {\sum\limits_{m = 0}^{m = N}{K_{m} \times e^{{- j}\;\omega\;\tau_{m}}}} )^{2}} = {{A^{2}(\omega)} \times e^{{- j}\; 2{\theta{(\omega)}}}}$is roundtrip multipath RF channel transfer function in the frequencydomain.

From equation 29, the roundtrip multipath channel has a larger number ofpaths than one-way channel multipath because the

$( {\sum\limits_{m = 0}^{m = N}{K_{m} \times e^{{- j}\;\omega\;\tau_{m}}}} )^{2}$expression in addition to the τ₀÷τ_(N) paths delays, includescombinations of these paths delays, for example: τ₀+τ₁, τ₀+τ₂ . . . ,τ₁+τ₂, τ₁±τ₃, . . . , etc.

These combinations dramatically increase the number of signals (complexexponentials). Hence the probability of very closely spaced (in time)signals will also increase and may lead to significant model sizeunderestimation. Thus, it is desirable to obtain one-way multipath RFchannel transfer function.

In preferred embodiment, the one-way amplitude values |Â(f_(n))| aredirectly obtainable from target/slave device. However, the one-way phasevalues ∠Â(f_(n)) cannot be measured directly. It is possible todetermine the phase of the one-way from the roundtrip phase measurementsobservation:

$( {\sum\limits_{m = 0}^{m = N}{K_{m} \times e^{{- j}\;\omega\;\tau_{m}}}} )^{2} = {{e^{{- j}\; 2{\theta{(\omega)}}}\mspace{14mu}{and}\mspace{14mu}( {\sum\limits_{m = 0}^{m = N}{K_{m} \times e^{{- j}\;\omega\;\tau_{m}}}} )} = e^{{- j}\;{\theta{(\omega)}}}}$

However, for each value of ω, there are two values of phase α(ω) suchthate ^(j2α(ω)) =e ^(jβ(ω))

A detailed description of resolving this ambiguity is shown below. Ifthe ranging signal different frequency components are close to eachother, then for most part the one-way phase can be found by dividing theroundtrip phase by two. Exceptions will include the areas that are closeto the “null”, where the phase can undergo a significant change evenwith small frequency step. Note: the “null” phenomena is where theincoming RF signals from multiple paths are destructively combining withthe DLOS path and each other, thus significantly reducing the receivedsignal strength and associated with it SNR.

Let h(t) be the one-way impulse response of a communications channel.The corresponding transfer function in the frequency domain is

$\begin{matrix}{{H(\omega)} = {{\int_{- \infty}^{\infty}{{h(t)}e^{{- j}\;\omega\; t}{dt}}} = {{A(\omega)}e^{j\;{\alpha{(\omega)}}}}}} & (30)\end{matrix}$

where A(ω)≥0 is the magnitude and α(ω) is the phase of the transferfunction. If the one-way impulse response is retransmitted back throughthe same channel as it is being received, the resulting two-way transferfunction isG(ω)=B(ω)e ^(jβ(ω)) =H ²(ω)=A ²)(ω)e ^(j2α(ω))  (31)

where B(ω)≥0. Suppose the two-way transfer function G(ω) is known forall ω in some open frequency interval (ω₁, ω₂). Is it possible todetermine the one-way transfer function H(ω) defined on (ω₁, ω₂) thatproduced G(ω)?

Since the magnitude of the two-way transfer function is the square ofthe one-way magnitude, it is clear thatA(ω)=√{square root over (B(ω))}  (32)

However, in trying to recover the phase of the one-way transfer functionfrom observation of G(ω), the situation is subtler. For each value of ω,there are two values of phase α(ω) such thate ^(j2α(ω)) =e ^(jβ(ω))  (33)

A large number of different solutions might be generated byindependently choosing one of two possible phase values for eachdifferent frequency ω.

The following theorems, which assume that any one-way transfer functionis continuous at all frequencies, help resolve this situation.

Theorem 1: Let I be an open interval of frequencies w containing nozeros of the two-way transfer function G(ω)=B(ω)e^(jβ(ω)). LetJ(ω)=√{square root over (B(ω))}e^(jγ(ω)) be a continuous function on Iwhere β(ω)=2γ(ω). Then J(ω) and −J(ω) are the one-way transfer functionswhich produce G(ω) on I, and there are no others.

Proof:

One of the solutions for the one-way transfer function is the functionH(ω)=√{square root over (B(ω)}e^(jα(ω)), continuous on I since it isdifferentiable on I, and where β(ω)=2α(ω). Since G(ω)≠0 on I, H(ω) andJ(ω) are nonzero on I. Then,

$\begin{matrix}{\frac{H(\omega)}{J(\omega)} = {\frac{\sqrt{B(\omega)}e^{j\;{\alpha{(\omega)}}}}{\sqrt{B(\omega)}e^{j\;{\gamma{(\omega)}}}} = e^{j{\lbrack{{\alpha{(\omega)}} - {\gamma{(\omega)}}}\rbrack}}}} & (34)\end{matrix}$

Since H(ω) and J(ω) are continuous and nonzero on I, their ratio iscontinuous on I, hence the right side of (34) is continuous on I. Theconditions β(ω)=2α(ω)=2γ(ω) imply that for each ω∈I, α(ω)−γ(ω) is either0 or π. However, α(ω)−γ(ω) cannot switch between these two valueswithout causing a discontinuity on the right side of (34). Thus, eitherα(ω)−γ(ω)=0 for all ω∈I, or α(ω)−γ(ω)=π for all ω∈I. In the first case,we get J(ω)=H(ω), and in the second we get J(ω)=−H(ω).

This theorem proves that to get a one-way solution on any open intervalI containing no zeros of the transfer function G(ω)=B(ω)e^(jβ(ω)), weform the function J(ω)=√{square root over (B(ω))}e^(jβ(ω)), choosing thevalues of γ(ω) satisfying β(ω)=2γ(ω) in such a way as to make J(ω)continuous. Since it is known that there is a solution having thisproperty, namely H(ω), it is always possible to do this.

An alternate procedure for finding a one-way solution is based on thefollowing theorem:

Theorem 2: Let H(ω)=A(ω)e^(jα(ω)) be a one-way transfer function and letI be an open interval of frequencies w containing no zeros of H(ω). Thenthe phase function α(ω) of H(ω) must be continuous on I.

Proof:

Let ω₀ be a frequency in the interval I. In FIG. 7, the complex valueH(ω₀) has been plotted as a point in the complex plane, and byhypothesis, H(ω)≠0. Let ε>0 be an arbitrarily small real number, andconsider the two angles of measure ε shown in the FIG. 7, as well as thecircle centered at H(ω₀) and tangent to the two rays OA and OB. Byassumption, H(ω) is continuous for all ω. Thus, if ω is sufficientlyclose to ω₀, the complex value H(ω) will lie in the circle, and it isseen that |α(ω)−α(ω₀)|<ε. Since ε>0 was chosen arbitrarily, we concludethat α(ω)→α(ω₀) as ω→ω₀, so that the phase function α(ω) is continuousat ω₀.

Theorem 3: Let I be an open interval of frequencies w containing nozeros of the two-way transfer function G(ω)=B (ω)e^(jβ(ω)). LetJ(ω)=√{square root over (B(ω))}e^(jγ(ω)) be a function on I whereβ(ω)=2γ(ω) and γ(ω) is continuous on I. Then J(ω) and −J(ω) are theone-way transfer functions which produce G(ω) on I, and there are noothers.

Proof:

The proof is similar to the proof of Theorem 1. We know that one of thesolutions for the one-way transfer function is the functionH(ω)=√{square root over (B(ω))}e^(jα(ω)), where β(ω)=2α(ω). Since G(ω)≠0on I, H(ω) and J(ω) are nonzero on I. Then,

$\begin{matrix}{\frac{H(\omega)}{J(\omega)} = {\frac{\sqrt{B(\omega)}e^{j\;{\alpha{(\omega)}}}}{\sqrt{B(\omega)}e^{j\;{\gamma{(\omega)}}}} = e^{j{\lbrack{{\alpha{(\omega)}} - {\gamma{(\omega)}}}\rbrack}}}} & (35)\end{matrix}$

By hypothesis γ(ω) is continuous on I and by Theorem 2 α(ω) is alsocontinuous on I. Thus, α(ω) γ(ω) is continuous on I. The conditionsβ(ω)=2α(ω)=2γ(ω) imply that for each ω∈I, α(ω)−γ(ω) is either 0 or π.However, α(ω)−γ(ω) cannot switch between these two values withoutbecoming discontinuous on I. Thus, either α(ω)−γ(ω)=0 for all ω∈I, orα(ω)−γ(ω)=π for all ω∈I. In the first case, we get J(ω)=H(ω), and in thesecond J(ω)=−H(ω).

Theorem 3 tells us that to get a one-way solution on any open interval Icontaining no zeros of the transfer function G(ω)=B (ω)e^(jβ(ω)), wesimply form the function J(ω)=√{square root over (B(ω)}e^(jγ(ω)),choosing the values of γ(ω) satisfying β(ω)=2γ(ω) in such a way as tomake the phase function γ(ω) continuous. Since it is known that there isa solution having this property, namely H(ω), it is always possible todo this.

Although the above theorems show how to reconstruct the two one-waytransfer functions which generate the two-way function G(ω), they areuseful only on a frequency interval I containing no zeros of G(ω). Ingeneral, G(ω) will be observed on a frequency interval (ω₁, ω₂) whichmay contain zeros. The following is a method that might get around thisproblem, assuming that there are only a finite number of zeros of G(ω)in (ω₁,ω₂), and that a one-way transfer function has derivatives of allorders on (ω₁,ω₂), not all of which are zero at any given frequency ω:

Let H(ω) be a one-way function that generates G(ω) on the interval(ω₁,ω₂), and assume that G(ω) has at least one zero on (ω₁,ω₂). Thezeros of G(ω) will separate (ω₁,ω₂) into a finite number of abuttingopen frequency intervals J₁, J₂, . . . , J_(n). On each such intervalthe solution H(ω) or −H(ω)) will be found using either Theorem 1 orTheorem 3. We need to “stitch together” these solutions so that thestitched solution is either H(ω) or −H(ω) across all of (ω₁,ω₂). Inorder to do this, we need to know how to pair the solutions in twoadjacent subintervals so that we aren't switching from H(ω) to −H(ω) orfrom −H(ω) to H(ω) in moving from one subinterval to the next.

We illustrate the stitching procedure starting with the first twoadjacent open subintervals J₁ and J₂. These subintervals will abut at afrequency ω₁ which is a zero of G(ω) (of course, ω₁ is not contained ineither subinterval). By our above assumption about the properties of aone-way transfer function, there must be a minimum positive integer nsuch that H^((n))(ω₁)≠0, where the superscript (n) denotes the n^(th)derivative. Then the limit of the n^(th) derivative of our one-waysolution in J₁ as ω→ω₁ from the left will be either H^((n)) (ω₁) or−H^((n))(ω₁) according to whether our solution in J₁ is H(ω) or H(ω).Similarly, the limit of the n^(th) derivative of our one-way solution inJ₂ as ω→ω₁ from the right will be either H^((n))(ω₁) or −H^((n))(ω₁)according to whether our solution in J₂ is H(ω) or −H(ω). SinceH^((n))(ω₁)≠0, the two limits will be equal if and only if the solutionsin J₁ and J₂ are both H(ω) or both −H(ω). If the left and right handlimits are unequal, we invert the solution in subinterval J₂. Otherwise,we don't.

After inverting the solution in subinterval J₂ (if necessary), weperform an identical procedure for subintervals J₂ and J₃ inverting thesolution in subinterval J₃ (if necessary). Continuing in this fashion,we eventually build up a complete solution on the interval (ω₁, ω₂).

It would be desirable that high-order derivatives of H(ω) not berequired in the above reconstruction procedure, since they are difficultto compute accurately in the presence of noise. This problem is unlikelyto occur, because at any zero of G(ω) it seems very likely that thefirst derivative of H(ω) will be nonzero, and if not, very likely thatthe second derivative will be nonzero.

In a practical scheme, the two-way transfer function G(ω) will bemeasured at discrete frequencies, which must be close enough together toenable reasonably accurate computation of derivatives near the zeros ofG(ω).

For RF-based distance measurements it is necessary to resolve an unknownnumber of closely spaced, overlapping, and noisy echoes of a rangingsignal with a priori known shape. Assuming that ranging signal is anarrow-band, in frequency domain this RF phenomena can be described(modeled) as a sum of a number of sine waves, each per multipathcomponent, and each with the complex attenuation and propagation delayof the path.

Taking the Fourier transform of the above mentioned sum will expressthis multipath model in the time domain. Exchanging the role of time andfrequency variables in this time domain expression, this multipath modelwill become harmonic signals spectrum in which the propagation delay ofthe path is transformed to a harmonic signal.

The super (high) resolution spectral estimation methods are designed todistinguish closely-placed frequencies in the spectrum and used forestimating the individual frequencies of multiple harmonic signals,e.g., paths delays. As a result, path delays can be accuratelyestimated.

The super resolution spectral estimation makes use of theeigen-structure of the covariance matrix of the baseband ranging signalsamples and covariance matrix intrinsic properties to provide a solutionto an underlying estimation of individual frequencies, e.g. pathsdelays. One of the eigen-structure properties is that the eigenvaluescan be combined and consequently divided into orthogonal noise andsignal eigenvectors, aka subspaces. Another eigen-structure property isthe rotation-invariant signal subspaces property.

The subspace decomposition technology (MUSIC, rootMUSIC, ESPRIT, etc.)relies on breaking the estimated covariance matrix of the observed datainto two orthogonal subspaces, the noise subspace and the signalsubspace. The theory behind the subspace decomposition methodology isthat the projection of the observable onto the noise subspace consistsof only the noise and the projection of the observable onto the signalsubspace consists of only the signal.

The spectral estimation methods assume that signals are narrow-band, andthe number of harmonic signals is also known, i.e., the size of thesignal subspace needs to be known. The size of the signal subspace iscalled as the model size. In general, it cannot be known in any detailand can change rapidly—particularly indoors—as the environment changes.One of the most difficult and subtle issues when applying any subspacedecomposition algorithm is the dimension of the signal subspace that canbe taken as the number of frequency components present, and which is thenumber multipath reflections plus the direct path. Because of real-worldmeasurement imperfections there always will be an error in the modelsize estimation, which in turn will result in loss of accuracy offrequencies estimation, i.e., distances.

To improve the distance measurement accuracy, one embodiment includessix features that advance the state of the art in the methodology ofsubspace decomposition high resolution estimation. Included is combiningtwo or more algorithms estimating individual frequencies by usingdifferent eigen-structure properties that further reduces the delay pathdetermination ambiguity.

Root Music finds the individual frequencies, that when the observable isprojected onto the noise subspace, minimizes the energy of theprojection. The Esprit algorithm determines the individual frequenciesfrom the rotation operator. And in many respects this operation is theconjugate of Music in that it finds the frequencies that, when theobservable is projected onto the signal subspace, maximizes the energyof the projection.

The model size is the key to both of these algorithms, and in practice,in a complex signal environment such as seen in indoor ranging—the modelsize which provides the best performance for Music and Esprit are ingeneral not equal, for reasons that will be discussed below.

For Music it is preferable to err on the side of identifying a basiselement of the decomposition as a “signal eigen value” (Type I Error).This will minimize the amount of signal energy that is projected on thenoise subspace and improve the accuracy. For Esprit—the opposite istrue—it is preferable to err on the side of identifying a basis elementof the decomposition as a “noise eigenvalue.” This is again a Type IError. This will minimize the impact of noise on the energy projectedonto the signal subspace. Therefore, the model size for Music will, ingeneral, be somewhat larger than that for Esprit.

Secondly, in a complex signal environment, there arise occasions where,with the strong reflections and the potential that the direct path is infact much weaker than some of the multipath reflections, the model sizeis difficult to estimate with sufficient statistical reliability. Thisissue is addressed by estimating a “base” model size for both Music andEsprit and the processing the observable data using Music and Esprit ina window of model sizes defined by the base model size for each. Thisresults in multiple measurements for each measurement.

The first feature of the embodiment is the use of the F-statistic toestimate the model size (see above). The second feature is the use ofdifferent Type I Error probabilities in the F-statistic for Music andEsprit. This implements the Type I Error differences between Music andEsprit as discussed above. The third feature is the use of a base modelsize and a window in order to maximize the probability of detecting thedirect path.

Because of the potentially rapidly changing physical and electronicenvironment, not every measurement will provide robust answers. This isaddressed by using cluster analysis on multiple measurements to providea robust range estimate. The fourth feature of the embodiment is the useof multiple measurements.

Because there are multiple signals present, the probability distributionof the multiple answers resulting from multiple measurements, each usingmultiple model sizes from both a Music and Esprit implementation, willbe multimodal. Conventional cluster analysis will not be sufficient forthis application. The fifth feature is the development of multimodalcluster analysis to estimate the direct range and equivalent range ofthe reflected multipath components. The sixth feature is the analysis ofthe statistics of the range estimates provided by the cluster analysis(range and standard deviation and combing those estimates that arestatistically identical. This results in a more accurate range estimate.

The abovementioned methods can be also used in wide bandwidth rangingsignal location-finding systems.

For the derivation of r(t) in the threshold method, starting withexpression (20), we obtain

$\begin{matrix}\begin{matrix}{{g(t)} = {( {a_{0} + {\sum\limits_{k = 1}^{M}{a_{k}\sin\; k\;{\pi\Delta}\; f\; t}}} )\sin\;{\pi( {{2N} + 1} )}\Delta\; f\; t}} \\{= {{a_{0}\sin\;{\pi( {{2N} + 1} )}\Delta\; f\; t} + {\sum\limits_{k = 1}^{M}{a_{k}\sin\;{\pi( {{2N} + 1} )}\Delta\; f\; t\;\sin\; k\;{\pi\Delta}\; f\; t}}}} \\{= {{a_{0}\sin\;{\pi( {{2N} + 1} )}\Delta\; f\; t} +}} \\{{\sum\limits_{k = 1}^{M}{\frac{1}{2}a_{k}\cos\;{\pi( {{2N} + 1 - k} )}\Delta\; f\; t}} - {\sum\limits_{k = 1}^{M}{\frac{1}{2}a_{k}\cos\;{\pi( {{2N} + 1 + k} )}\Delta\; f\; t}}} \\{= {{a_{0}\sin\; 2{\pi( {N + \frac{1}{2}} )}\Delta\; f\; t} +}} \\{{\sum\limits_{k = 1}^{M}{\frac{1}{2}a_{k}\cos\; 2{\pi( {N + \frac{1}{2} - \frac{k}{2}} )}\Delta\; f\; t}} -} \\{\sum\limits_{k = 1}^{M}{\frac{1}{2}a_{k}\cos\; 2{\pi( {N + \frac{1}{2} + \frac{k}{2}} )}\Delta\; f\; t}}\end{matrix} & ( {A\; 1} )\end{matrix}$

where the trigonometric identity

$\sin\; x\;\sin\;{y = {{\frac{1}{2}{\cos( {x - y} )}} - {\frac{1}{2}{\cos( {x + y} )}}}}$is used.

Except for a₀, the coefficients a_(k) are zero for even k. The reasonfor this is that on the interval I, the function 1/sin πΔft that we aretrying to approximate by h(t) is even about the center of I, but thebasis functions sin kπΔft for even k, k≠0, are odd about the center ofI, hence are orthogonal to 1/sin πΔft on I. Thus, we can make thesubstitution k=2n+1 and let M be an odd positive integer. In fact, wewill let M=2N+1. This choice has been experimentally determined toprovide a good amount of cancellation of the oscillations in theinterval I.

$\begin{matrix}{{g(t)} = {{a_{0}\sin\; 2{\pi( {N + \frac{1}{2}} )}\Delta\; f\; t} + {\sum\limits_{n = 0}^{N}{\frac{1}{2}a_{{2n} + 1}\cos\; 2{\pi( {N - n} )}\Delta\; f\; t}} - {\sum\limits_{n = 0}^{N}{\frac{1}{2}a_{{2n} + 1}\cos\; 2{\pi( {N + n + 1} )}\Delta\; f\; t}}}} & ({A2})\end{matrix}$

Now we make the substitution k=N−n in the first summation and k=N+n+1 inthe second summation to obtain

$\begin{matrix}\begin{matrix}{{g(t)} = {{a_{0}\sin\; 2{\pi( {N + \frac{1}{2}} )}\Delta\; f\; t} + {\sum\limits_{n = 0}^{N}{\frac{1}{2}a_{{2{({N - k})}} + 1}\cos\; 2\pi\; k\;\Delta\; f\; t}} -}} \\{\sum\limits_{k = {N + 1}}^{{2N} + 1}{\frac{1}{2}a_{{2{({k - N})}} - 1}\cos\; 2\pi\; k\;\Delta\; f\; t}} \\{= {{a_{0}\sin\; 2{\pi( {N + \frac{1}{2}} )}\Delta\; f\; t} + {\frac{1}{2}a_{{2N} + 1}} + {\sum\limits_{k = 1}^{N}{\frac{1}{2}a_{{2{({N - k})}} + 1}\cos\; 2\pi\; k\;\Delta\; f\; t}} -}} \\{\sum\limits_{k = {N + 1}}^{{2N} + 1}{\frac{1}{2}a_{{2{({k - N})}} - 1}\cos\; 2\pi\; k\;\Delta\; f\; t}}\end{matrix} & ({A3})\end{matrix}$

Subtracting g(t) from s(t) results in

$\begin{matrix}\begin{matrix}{{r(t)} = {{s(t)} - {g(t)}}} \\{= {1 + {2{\sum\limits_{k = 1}^{N}{\cos\; 2\pi\; k\;\Delta\; f\; t}}} - {\frac{1}{2}a_{{2N} + 1}} - {\sum\limits_{k = 1}^{N}{\frac{1}{2}a_{{2{({N - k})}} + 1}\cos\; 2\pi\; k\;\Delta\; f\; t}} +}} \\{{\sum\limits_{k = {N + 1}}^{{2N} + 1}{\frac{1}{2}a_{{2{({k - N})}} - 1}\cos\; 2\pi\; k\;\Delta\; f\; t}} - {a_{0}\sin\; 2\;{\pi( {N + \frac{1}{2}} )}\Delta\; f\; t}}\end{matrix} & ({A4})\end{matrix}$

Now let

$\begin{matrix}{{b_{0} = {1 - {\frac{1}{2}a_{{2N} + 1}}}}{{b_{k} = {{2 - {\frac{1}{2}a_{{2{({N - k})}} + 1}\mspace{14mu}{for}\mspace{14mu} k}} = 1}},2,\ldots\mspace{11mu},N}{{b_{k} = {{{- \frac{1}{2}}a_{{2{({k - N})}} - 1}\mspace{14mu}{for}\mspace{14mu} k} = {N + 1}}},{N + 2},\ldots\mspace{11mu},{{2N} + 1}}{c = {- a_{0}}}} & ({A5})\end{matrix}$

Then (A4) can be written as

$\begin{matrix}{{r(t)} = {b_{0} + {\sum\limits_{k = 1}^{{2N} + 1}{b_{k}\cos\; 2\;\pi\; k\;\Delta\; f\; t}} + {c\;\sin\; 2\;{\pi( {N + \frac{1}{2}} )}\Delta\; f\; t}}} & ({A6})\end{matrix}$

The present embodiments relate to a positioning/locating method inwireless communication and other wireless networks that substantiallyobviate one or more of the disadvantages of the related art. The presentembodiments advantageously improve the accuracy of tracking and locatingfunctionality in multiple types of wireless network by utilizingmulti-path mitigation processes, techniques and algorithms, described inU.S. Pat. No. 7,872,583, These wireless networks include WirelessPersonal Area Networks (WPGAN) such as ZigBee and Blue Tooth, wirelesslocal area network (WLAN) such as WiFi and UWB, Wireless MetropolitanArea Networks, (WMAN) typically consisting of multiple WLANs, WiMaxbeing the primary example, wireless Wide Area Networks (WAN) such asWhite Space TV Bands, and Mobile Devices Networks (MDN) that aretypically used to transmit voice and data. MDNs are typically based onGlobal System for Mobile Communications (GSM) and PersonalCommunications Service (PCS) standards. A more recent MDN is based onthe Long Term Evolution (LTE) standard. These wireless networks aretypically comprised of a combination of devices, including basestations, desktop, tablet and laptop computers, handsets, smartphones,actuators, dedicated tags, sensors as well as other communication anddata devices (generally, all these devices are referred to as “wirelessnetwork devices”).

Existing location and positioning information solutions use multipletechnologies and networks, including GPS, AGPS, Cell Phone TowerTriangulation, and Wi-Fi. Some of the methods used to derive thislocation information include RF Fingerprinting, RSSI, and TDOA. Althoughacceptable for the current E911 requirements, existing location andranging methods do not have the reliability and accuracy required tosupport the upcoming E911 requirements as well as LBS and/or RTLSapplications requirements, especially indoors and urban environments.

The methods described in U.S. Pat. No. 7,872,583 significantly improvethe ability to accurately locate and track targeted devices within asingle wireless network or a combination of multiple wireless networks.The embodiment is a significant improvement to the existingimplementation of tracking and location methods used by wirelessnetworks that use Enhanced Cell-ID and OTDOA (Observed Time Differenceof Arrival), including DL-OTDOA (Downlink OTDOA), U-TDOA, UL-TDOA andothers

Cell ID location technique allows estimating the position of the user(UE-User Equipment) with the accuracy of the particular sector coveragearea. Thus, the attainable accuracy depends on the cell (base station)sectoring scheme and antenna beam-width. In order to improve accuracy,the Enhanced Cell ID technique adds RTT (Round Trip Time) measurementsfrom the eNB. Note: Here, the RTT constitutes the difference betweentransmission of a downlink DPCH Dedicated Physical Channel,(DPDCH)/DPCCH: Dedicated Physical Data Channel/Dedicated PhysicalControl Channel) frame and the beginning of a corresponding uplinkphysical frame. In this instance the abovementioned frame(s) act as aranging signal. Based on the information of how long this signalpropagates from eNB to the UE, the distance from eNB can be calculated(see FIG. 10).

In the Observed Time Difference of Arrival (OTDOA) technique the time ofarrival of the signal coming from neighboring base stations (eNB) iscalculated. The UE position can be estimated in the handset (UE-basedmethod) or in the network (NT-based, UE-assisted method) once thesignals from three base stations are received. The measured signal isthe CPICH (Common Pilot Channel). The propagation time of signals iscorrelated with a locally generated replica. The peak of correlationindicates the observed time of propagation of the measured signal. Timedifference of arrival values between two base stations determines ahyperbola. At least three reference points are needed to define twohyperbolas. The location of the UE is in the intersection of these twohyperbolas (see FIG. 11).

Idle Period Downlink (IPDL) is further OTDOA enhancement. The OTDOA-IPDLtechnique is based on the same measurements as the regular OTDOA Timemeasurements are taken during idle periods, in which serving eNB ceasesits transmissions and allows the UE within the coverage of this cell tohear pilots coming from distant eNB(s). Serving eNB provides idleperiods in continuous or burst mode. In the continuous mode, one idleperiod is inserted in every downlink physical frame (10 ms). In theburst mode, idle periods occur in a pseudo-random way. Furtherimprovement is obtained via Time Aligned IPDL (TA-IPDL). Time alignmentcreates a common idle period, during which, each base station willeither cease its transmission or transmit the common pilot. The pilotsignal measurements will occur in idle period. There are several othertechniques that may further enhance the DL OTDOA-IPDL method, forexample Cumulative Virtual Blanking, UTDOA (Uplink TDOA), etc. All thesetechniques improve the ability to hear other (non-serving) eNB(s).

One significant drawback of the OTDOA based techniques is that the basestations timing relationships must be known, or measured (synchronized),for this method to be viable. For unsynchronized UMTS networks the 3GPPstandard offers suggestion of how this timing may be recovered. However,networks operators are not implementing such solution. As a result, analternative that uses the RTT measurements in lieu of the CPICH signalmeasurements was proposed (see U.S. Patent Publication No. 20080285505,John Carlson et al., SYSTEM AND METHOD FOR NETWORK TIMING RECOVERY INCOMMUNICATIONS NETWORKS).

All abovementioned methods/techniques are based on the terrestrialsignals time of arrival and/or time difference of arrival measurements(RTT, CPICH, etc.). An issue with such measurements is that these areseverely impacted by the multi-path. This, in turn, significantlydegrades the abovementioned methods/techniques locate/track accuracy(see Jakub Marek Borkowski: Performance of Cell ID+RTT HybridPositioning Method for UMTS).

One Multi-path mitigation technique uses detections/measurements fromexcess number of eNB(s) or Radio Base Stations (RBS). The minimum isthree, but for multipath mitigation the number of RBS's required is atleast six to eight (see METHOD AND ARRANGEMENT FOR DL-OTDOA (DOWNLINKOBSERVED TIME DIFFERENCE OF ARRIVAL) POSITIONING IN A LTE (LONG TERMEVOLUTION) WIRELESS COMMUNICATIONS SYSTEM, WO/2010/104436). However, theprobability of an UE hearing from this large number of eNB(s) is muchlower than from three eNB(s). This is because with large number of RBS(eNBs) there will be several ones that are far away from the UE and thereceived signal from these RBS may fall below the UE receiversensitivity level or the received signal will have low SNR.

In case of RF reflections (e.g., multi-path), multiple copies of the RFsignal with various delay times are superimposed onto the DLOS (DirectLine of Site) signal. Because CPICH, uplink DPCCH/DPDCH and othersignals that are used in various CELL ID and OTDOA methods/techniques,including the RTT measurements, are of a limited bandwidth the DLOSsignal and reflected signals cannot be differentiated without propermulti-path processing/mitigation; and without this multi-path processingthese reflected signals will induce an error in the estimated timedifference of arrival (TDOA) and time of arrival (TOA) measurements,including RTT measurements.

For example, 3 G TS 25.515 v.3.0.0 (199-10) standards define the RTT as“ . . . the difference between transmission of a downlink DPCH frame(signal) and the reception of the beginning (first significant path) ofthe corresponding uplink DPCCH/DPDCH frame (signal) from UE”. Thestandard does not define what constitutes this “first significant path”.The standard goes on noting that “The definition of the firstsignificant path needs further elaboration”. For example, in heavymultipath environment it is a common occurrence whereby the DLOS signal,which is the first significant path, is severely attenuated (10 dB 20dB) relatively to one or more reflected signal(s). If the “firstsignificant path” is determined by measuring the signal strength, it maybe one of the reflected signal(s) and not the DLOS signal. This willresult in erroneous TOA/DTOA/RTT measurement(s) and loss of locatingaccuracy.

In prior wireless networks generations the locating accuracy was alsoimpacted by the low sampling rate of frames (signals) that are used bythe locate methods—RTT, CPCIH and other signals. The current third andfollowing wireless network generations have much higher sampling rate.As a result, in these networks the locating accuracy real impact is fromthe terrestrial RF propagation phenomena (multipath).

The embodiment can be used in all wireless networks that employreference and/or pilot signals, and/or synchronization signals,including simplex, half-duplex and full duplex modes of operation. Forexample, the embodiment operates with wireless networks that employ OFDMmodulation and/or its derivatives. Thus, the embodiment operates withLTE networks.

It is also applicable to other wireless networks, including WiMax, WiFi,and White Space. Other wireless networks that do not use referenceand/or pilot or synchronization signals may employ one or more of thefollowing types of alternate modulation embodiments as described in U.S.Pat. No. 7,872,583: 1) where a portion of frame is dedicated to theranging signal/ranging signal elements as described in U.S. Pat. No.7,872,583; 2) where the ranging signal elements (U.S. Pat. No.7,872,583) are embedded into transmit/receive signals frame(s); and 3)where the ranging signal elements (described in U.S. Pat. No. 7,872,583)are embedded with the data.

These alternate embodiments employ multi-path mitigation processor andmulti-path mitigation techniques/algorithms described in U.S. Pat. No.7,872,583 and can be used in all modes of operation: simplex,half-duplex and full duplex.

It is also likely that multiple wireless networks will, at the sametime, utilize the preferred and/or alternate embodiments. By way ofexample, a smart phone can have Blue Tooth, WiFi, GSM and LTEfunctionality with the capability of operating on multiple networks atthe same time. Depending on application demands and/or networkavailability, different wireless networks can be utilized to providepositioning/locating information.

The proposed embodiment method and system leverages the wireless networkreference/pilot and/or synchronization signals. Furthermore, thereference/pilot signal/synchronization signals measurements might becombined with RTT (Round Trip Time) measurements or system timing.According to an embodiment, RF-based tracking and locating isimplemented on 3GPP LTE cellular networks, but could be also implementedon other wireless networks, for example WiMax, Wi-Fi, LTE, sensorsnetworks, etc. that employ a variety of signaling techniques. Both theexemplary and mentioned above alternative embodiments employ multi-pathmitigation method/techniques and algorithms that are described in U.S.Pat. No. 7,872,583. The proposed system can use software implementeddigital signal processing.

The system of the embodiment leverages User Equipment (UE), e.g. cellphone or smart phone, hardware/software as well as Base Station (NodeB)/enhanced Base Station (eNB) hardware/software. A base stationgenerally consists of transmitters and receivers in a cabin or cabinetconnected to antennas by feeders. These base stations include, MicroCell, Pico Cell, Macro Cell, Umbrella Cell, Cell Phone towers, Routersand Femtocells. As a result, there will be little or no incremental costto the UE device and overall system. At the same time the locateaccuracy will be significantly improved.

The improved accuracy comes from the multipath mitigation that isprovided by the present embodiments and U.S. Pat. No. 7,872,583. Theembodiments use multi-path mitigation algorithms, networkreference/pilot and/or synchronization signals and network node (eNB).These might be supplemented with RTT (Round Time Trip) measurements. Themulti-path mitigation algorithms are implemented in UE and/or basestation (eNB), or both: UE and eNB

The embodiments advantageously use the multi-path mitigationprocessor/algorithms (see U.S. Pat. No. 7,872,583) that allow separatingthe DLOS signal and reflected signals, even when DLOS signal issignificantly attenuated (10 dB-20 dB lower) relatively to one or morereflected signals. Thus, the embodiments significantly lower the errorin the estimated ranging signal DLOS time-of-flight and consequentlyTOA, RTT and DTOA measurements. The proposed multi-path mitigation andDLOS differentiating (recognizing) method can be used on all RF bandsand wireless systems/networks. And it can support variousmodulation/demodulation techniques, including Spread Spectrumtechniques, such as DSS (Direct Spread Spectrum) and FH (FrequencyHopping).

Additionally, noise reduction methods can be applied in order to furtherimprove the method's accuracy. These noise reduction methods caninclude, but are not limited to, coherent summing, non-coherent summing,Matched filtering, temporal diversity techniques, etc. The remnants ofthe multi-path interference error can be further reduced by applying thepost-processing techniques, such as, maximum likelihood estimation(e.g., Viterbi Algorithm), minimal variance estimation (Kalman Filter),etc.

In present embodiments the multi-path mitigation processor andmulti-path mitigation techniques/algorithms do not change the RTT, CPCIHand other signals and/or frames. The present embodiments leveragewireless network reference, pilot and/or synchronization signals thatare used to obtain the channel response/estimation. The invention usesthe channel estimation statistics that is generated by UE and/or eNB(see Iwamatsu et al., APPARATUS FOR ESTIMATING PROPAGATION PATHCHARACTERISTICS, US 2003/008156; U.S. Pat. No. 7,167,456 B2).

LTE networks use specific (non-data) reference/pilot and/orsynchronization s signals (known signals) that are transmitted in everydownlink and uplink subframe, and might span entire cell bandwidth. Forsimplicity from now on we will refer to reference/pilot andsynchronization signals as reference signals. An example of the LTEreference signals is in FIG. 9 (these signals are interspersed among LTEresource elements). From FIG. 2, reference signals (symbols) aretransmitted every sixth subcarrier. Further, reference signals (symbols)are staggered in both time and frequency. In total, reference signalsare covering every third subcarrier.

These reference signals are used in the initial cell search by the UE,downlink signal strength measurements, scheduling and handover, etc.Included in the reference signals are UE-specific reference signals forchannel estimation (response determination) for coherent demodulation.In addition to the UE-specific reference signals, other referencesignals may be also used for channel estimation purposes, (see Chen etal., US patent publication No. 2010/0091826 A1).

LTE employs the OFDM (Orthogonal Frequency Division Multiplexing)modulation (technique). In LTE the ISI (Inter Symbol Interference)caused by multipath is handled by inserting Cyclic prefix (CP) at thebeginning of each OFDM symbol. The CP provides enough delay so thatdelayed reflected signals of the previous OFDM symbol will die outbefore reaching the next OFDM symbol.

An OFDM symbol consists of multiple very tightly spaced subcarriers.Inside the OFDM symbol time-staggered copies of the current symbol(caused by multipath) result in Inter Carrier Interference (ICI). In LTEthe ICI is handled (mitigated) by determining the multipath channelresponse and correcting the channel response in the receiver.

In LTE the multipath channel response (estimation) is computed in thereceiver from subcarriers bearing the reference symbols. Interpolationis used to estimate the channel response on the remaining subcarriers.The channel response is calculated (estimated) in form of channelamplitude and phase. Once the channel response is determined (byperiodic transmission of known reference signals), the channeldistortion caused by multipath is mitigated by applying an amplitude andphase shift on a subcarrier-by-subcarrier basis (see Jim Zyren, Overviewof the 3GPP Long Term Evolution Physical Layer, white paper).

LTE multipath mitigation is designed to remove the ISI (by inserting aCyclic Prefix) and ICI, but not to separate the DLOS signal fromreflected signals. For example, time-staggered copies of the currentsymbol make each modulated subcarrier signals spread in time, thuscausing ICI. Correcting multipath channel response using theabovementioned LTE technique will shrink modulated subcarrier signals intime, but this type of correction does not guarantee that the resultingmodulated subcarrier signals (inside the OFDM symbol) are DLOS signals.If DLOS modulated subcarrier signals are significantly attenuatedrelatively to delayed reflected signal(s), the resulting output signalwill be the delayed reflected signal(s) and the DLOS signal will belost.

In LTE compliant receiver, further signal processing includes DFT(Digital Fourier Transformation). It is well known that DFT technique(s)can resolve (remove) only copies of signal(s) that are delayed for timesthat are longer than or equal to the time that is inversely proportionalto the signal and/or channel bandwidth. This method accuracy may beadequate for an efficient data transfer, but not accurate enough forprecise distance measurement in a heavy multipath environment. Forexample, to achieve thirty meters accuracy, the signal and receiverchannel bandwidths should be larger than or equal to ten megahertz (1/10 MHz=100 ns.). For better accuracy the signal and receiver channelbandwidths should be wider—one hundred megahertz for three meters.

However, CPICH, uplink DPCCH/DPDCH and other signals that are used invarious CELL ID and OTDOA methods/techniques, including the RTTmeasurements, as well as the LTE received signal subcarriers havebandwidths that are significantly lower than ten megahertz. As a result,the currently employed (in LTE) method/technique will produce locatingerrors in the range of 100 meters.

To overcome the abovementioned limitations the embodiments use a uniquecombination of implementations of subspace decomposition high resolutionspectral estimation methodologies and multimodal cluster analysis. Thisanalysis and related multi-path mitigation method/techniques andalgorithms, described in U.S. Pat. No. 7,872,583, allow a reliable andaccurate separation of DLOS path from other reflected signals paths.

Compared to methods/techniques used in the LTE, in a heavy multipathenvironment this method/techniques and algorithms (U.S. Pat. No.7,872,583) deliver 20× to 50× accuracy improvement in the distancemeasurement via reliable and accurate separation of DLOS path from othermulti-path (MP) paths.

Methods/techniques and algorithms described in U.S. Pat. No. 7,872,583require ranging signal complex amplitude estimation. Accordingly, theLTE reference signals used for channel estimation (responsedetermination) as well as other reference signals (including pilotand/or synchronization signals, can be also construed as a rangingsignal in methods/techniques and algorithms described in U.S. Pat. No.7,872,583. In this case the ranging signal complex amplitude is thechannel response that is calculated (estimated) by the LTE receiver inform of amplitude and phase. In other words, the channel responsestatistics that is calculated (estimated) by the LTE receiver canprovide complex amplitude information that is required by themethod/techniques and algorithms described in U.S. Pat. No. 7,872,583.

In ideal open space RF propagation environment with no multipath thephase change of the received signal (ranging signal), e.g. channelresponse phase, will be directly proportional to the signal's frequency(a straight line); and the RF signal time-of-flight (propagation delay)in such environment can be directly computed from the phase vs.frequency dependency by computing first derivative of the phase vs.frequency dependency. The result will be the propagation delay constant.

In this ideal environment the absolute phase value at initial (or any)frequency is not important because the derivative is not affected by thephase absolute values.

In a heavy multipath environment the received signal phase change vs.frequency is a complicated curve (not a straight line); and the firstderivative does not provide information that could be used for accurateseparation of DLOS path from other reflected signals paths. This is thereason for employing multipath mitigation processor andmethod(s)/techniques and algorithms described in U.S. Pat. No.7,872,583.

If the phase and frequency synchronization (phase coherency) achieved ina given wireless network/system is very good, then multipath mitigationprocessor and method(s)/techniques and algorithms described in U.S. Pat.No. 7,872,583 will accurately separate DLOS path from other reflectedsignals paths and determine this DLOS path length (time-of-flight).

In this phase coherent network/system no additional measurements arerequired. In other words, one way ranging (simplex ranging) can berealized.

However, if the degree of synchronization (phase coherency) achieved ina given wireless network/system is not accurate enough, then in a heavymultipath environment the received signal phase and amplitude change vs.frequency might be very similar for measurements conducted at two ormore different locations (distances). This phenomenon might lead to anambiguity in received signal DLOS distance (time-of-flight)determination.

To resolve this ambiguity it is necessary to know the actual (absolute)phase value for at least one frequency.

However, the amplitude and phase vs. frequency dependency that iscomputed by the LTE receiver does not include an actual phase valuebecause all amplitude and phase values are computed from thedownlink/uplink reference signals, e.g. relative to each other. Thus,the amplitude and phase of the channel response that is calculated(estimated) by the LTE receiver needs actual phase value at least at onefrequency (subcarrier frequency).

In LTE this actual phase value can be determined from one or more RTTmeasurement(s), TOA measurements; or

from time-stamping of one or more received reference signals, providedthat 1) these time stamps of transmitting these signals by eNB are alsoknown at the receiver (or vice versa), 2) the receiver and eNB clocksare well synchronized in time, and/or 3) by using multilaterationtechniques.

All of the above methods provide the time-of-flight values of one ormore reference signals. From the time-of-flight values and frequenciesof these reference signals actual phase values at one or morefrequencies can be calculated.

The present embodiments achieve a highly accurate DLOS distancedetermination/locating in a heavy multipath environment by combiningmulti-path mitigation processor, method(s)/techniques and algorithmsdescribed in U.S. Pat. No. 7,872,583 with: 1) the amplitude and phasevs. frequency dependency that is computed by the LTE UE and/or eNBreceiver or 2) a combination of the amplitude and phase vs. frequencydependency that is computed by the LTE UE and/or eNB receiver and actualphase value(s) for one or more frequencies obtained via RTT and/or TOA;and/or time-stamping measurements.

In these cases the actual phase value(s) is affected by the multipath.However, this does not impact the performance of methods/techniques andalgorithms described in U.S. Pat. No. 7,872,583.

In LTE RTT/TOA/TDOA/OTDOA, including DL-OTDOA, U-TDOA, UL-TDOA, etc.,measurements can be carried out with the resolution of 5 meters. RTTmeasurements are carried during dedicated connections. Thus, multiplesimultaneous measurements are possible when UE is in handover state andtimes when UE periodically collects and reports measurements back to theUE, in which the DPCH frames are exchanged between the UE and differentnetworks (base stations). Similar to RTT, TOA measurements provide thesignal's time-of-flight (propagation delay), but TOA measurements cannotbe made simultaneously (Jakub Marek Borkowski: Performance of CellID+RTT Hybrid Positioning Method for UMTS).

In order to locate UE on plane DLOS distances have to be determined atleast from/to three eNB(s). To locate UE in three-dimensional spaceminimum four DLOS distances from/to four eNB(s) would have to bedetermined (assuming that at least one eNB is not on the same plane).

An example of UE positioning method is shown in FIG. 1.

In case of very good synchronization RTT measurements are not required.

If the degree of synchronization is not accurate enough, then methodslike OTDOA, Cell ID+RTT and others, for example AOA (Angle-of-Arrival)and its combinations with other methods, can be used for the UElocating.

The Cell ID+RTT track-locate method accuracy is impacted by themultipath (RTT measurements) and the eNB (base station) antennabeamwidth. Base stations antennas beamwidths are between 33 and 65degrees. These wide beamwidths results in locating error of 50-150meters in urban areas (Jakub Marek Borkowski: Performance of Cell ID+RTTHybrid Positioning Method for UMTS). Considering that in a heavymultipath environment the current LTE RTT distance measurement averageerror is approximately 100 meters, the overall expected average locateerror of the currently employed by LTE Cell ID+RTT method isapproximately 150 meters.

One of the embodiments is the UE locating based on the AOA method,whereby one or more reference signals from the UE is used for the UElocate purposes. It involves an AOA determination device location fordetermining the DLOS AOA. The device can be collocated with the basestation and/or installed at another one or more locations independentfrom the base station location. The coordinates of these locations arepresumably known. No changes are required on the UE side.

This device includes a small antenna array and is based on a variationof the same multipath mitigation processor, method(s)/techniques andalgorithms described in U.S. Pat. No. 7,872,583. This one possibleembodiment has the advantage of precise determination (very narrowbeamwidth) of the AOA of the DLOS RF energy from an UE unit.

In one other option this added device is receive only device. As aresult, its size/weight and cost are very low.

The combination of embodiments in which accurate DLOS distancemeasurements are obtained and embodiments in which an accurate DLOS AOAdetermination can be made will greatly improve the Cell ID+RTTtrack-locate method precision 10× or greater. Another advantage of thisapproach is that the UE location can be determined at any moment with asingle tower, (does not require placing UE in soft handover mode).Because an accurate location fix can be obtained with a single towerthere is no need to synchronize multiple cell towers. Another option ofdetermining the DLOS AOA is to use the existing eNB antenna array andthe eNB equipment. This option may further lower the cost ofimplementation of the improved Cell ID+RTT method. However, because eNBantennas are not designed for the locating applications, the positioningaccuracy may be degraded. Also, network operators may be unwilling toimplement required changes in base station (software/hardware).

In the LTE (Evolved Universal Terrestrial Radio Access (E-UTRA);Physical channels and modulation; 3GPP TS 36.211 Release 9 technicalSpecification) Positioning Reference Signals (PRS), were added. Thesesignals are to be used by the UE for the DL-OTDA (Downlink OTDOA),positioning. Also, this release 9 requires eNB(s) to be synchronized.Thus, clearing the last obstacle for OTDOA methods (see paragraph 274above). The PRS improves UE hear-ability at UE of multiple eNBs. Note:Release 9 did not specify the eNB synchronization accuracy (someproposals: 100 ns.).

The U-TDOA/UL-TDOA are in a study phase; to be standardized in 2011.

The DL-OTDOA method (in Release 9) is detailed in the US patent US2011/0124347 A1 (Method and Apparatus for UE positioning in LTEnetworks, Chen, at al.). The Release 9 DL-OTDOA suffers from themultipath. Some of the multipath mitigation can be achieved viaincreased PRS signal bandwidth. However, the trade-off is increasedscheduling complexity and longer times between UE positions fixes.Moreover, for networks with limited operating bandwidth, for example 10MHz, the best possible accuracy is 100 meters, see Chen, Table 1.

The above numbers are the best possible case. Other cases, especiallywhen the DLOS signal strength is significantly lower (10-20 dB) comparedto the reflected signal(s) strength, result in significantly larger(2×-4×) of the abovementioned locate/ranging errors.

Embodiments described herein allow for up to 50× ranging/locate accuracyimprovement for a given signal bandwidth over the performance achievedby the Release 9 DL-OTDOA method and the UL-PRS method of Chen et al.described in the Background section. Thus, applying embodiments of themethods described herein to the Release 9 PRS processing reduces thelocate error down to 3 meters or better in 95% of all possible cases. Inaddition, this accuracy gain will reduce the scheduling complexity andthe time between UE position fixes.

With the embodiments described herein further improvements for the OTDOAmethod are possible. For example, the ranging to the serving cell can bedetermined from other serving cells' signals, thus improving theneighboring cells hearability and reducing the scheduling complexity,including the time between UE positions fixes.

Embodiments also enable the accuracy of the U-TDOA method and UL-TDOAfrom Chen et al. (described in the Background) to be improved up to 50times. Applying embodiments to the Chen's UL-TDOA variant, reduces thelocate error down to 3 meters or better in 95% of all possible cases.Moreover, this accuracy gain further reduces the scheduling complexityand the time between UE positions fixes.

Again, with the present embodiments, Chen's UL-TDOA method accuracy canbe improved up to 50×. Thus, applying the present embodiments to theChen's U-TDOA variant, will reduce the locate error down to 3 meters orbetter in 95% of all possible cases. Moreover, this accuracy gain willfurther reduce the scheduling complexity and the time between UEpositions fixes.

The abovementioned DL-TDOA and U-TDOA/UL-TDOA methods rely on one-waymeasurements (ranging). Present embodiments and practically all otherranging technologies require that the PRS and/or other signals used inthe process of one-way ranging would be frequency and phase coherent.The OFDM based systems, like LTE, are frequency coherent. However, theUE units and eNB(s) are not phase or time synchronized by a commonsource—like UTC, to a couple nanoseconds, e.g. there exists a randomphase adder.

To avoid the phase coherency impact on the ranging accuracy, theembodiment of the multipath processor calculates the differential phasebetween the ranging signal(s), e.g. reference signals, individualcomponents (subcarriers). This eliminates the random phase term adder.

As identified above in the discussion of Chen et al., applying theembodiments described herein result in significant accuracy improvementin indoor environments compared to the performance achieved by Chen etal. For example, according to Chen, at al. the DL-OTDOA and/orU-TDOA/UL-TDOA are mostly for outdoor environments, indoors (buildings,campuses, etc.) the DL-OTDOA and U-TDOA technologies may not performwell. Several reasons are noted (see Chen, #161-164), including theDistributed Antenna Systems (DAS) that are commonly employed indoors,whereby each antenna does not have a unique ID.

The embodiment described below operates with wireless networks thatemploy OFDM modulation and/or its derivatives; and reference/pilot/andor synchronization signals. Thus, the embodiment described belowoperates with LTE networks and it is also applicable to other wirelesssystems and other wireless networks, including other types ofmodulation, with or without reference/pilot/and/or synchronizationsignals.

The approach described herein is also applicable to other wirelessnetworks, including WiMax, WiFi, and White Space. Other wirelessnetworks that do not use reference/pilot and/or synchronization signalsmay employ one or more of the following types of alternate modulationembodiments as described in U.S. Pat. No. 7,872,583: 1) where a portionof frame is dedicated to the ranging signal/ranging signal elements; 2)where the ranging signal elements are embedded into transmit/receivesignals frame(s); and 3) where the ranging signal elements are embeddedwith the data.

Embodiments of the multipath mitigation range estimation algorithmdescribed herein (also described in U.S. Pat. Nos. 7,969,311 and8,305,215) works by providing estimates of the ranges in the ensemblemade up of the direct path (DLOS) of a signal plus the multipathreflections.

The LTE DAS system produces multiple copies of the same signal seen atvarious time offsets to a mobile receiver (UE). The delays are used touniquely determine geometric relationships between the antennas and themobile receiver. The signal seen by the receiver resembles that seen ina multipath environment—except the major “multipath” components resultfrom the sum of the offset signals from the multiple DAS antennas.

The signal ensemble seen by the receiver is identical to the type ofsignal ensemble embodiments are designed to exploit—except that in thiscase the major multipath components are not traditional multipath. Thepresent multipath mitigation processor (algorithms) is capable ofdetermining the attenuation and propagation delay of the DLOS and eachpath, e.g. reflection, (see equations 1-3 and associated descriptions).While multipath can be present because of the dispersive RF channel(environment), the major multipath components in this signal ensembleare associated with transmissions from multiple antennas. Embodiments ofthe present multipath algorithm can estimate these multipath components,isolate the ranges of the DAS antennas to the receiver, and providerange data to the location processor (implemented in software).Depending on the antenna placing geometry, this solution can provideboth X, Y and X, Y, Z location coordinates.

As a result, present embodiments do not require any hardware and/or newnetwork signal(s) additions. Moreover, the positioning accuracy can besignificantly improved by 1) mitigating the multipath and 2) in case ofactive DAS the lower bound of positioning error can be drasticallyreduced, such as reducing from approximately 50 meters to approximately3 meters.

It is assumed that the position (location) of each antenna of a DAS isknown. The signal propagation delay of each antenna (or relative toother antenna) also has to be determined (known).

For active DAS systems the signal propagation delay may be determinedautomatically, using the loopback techniques, whereby the known signalis sent round trip and this round trip time is measured. This loopbacktechnique also eliminates the signal propagation delay changes (drift)with temperature, time, etc.

Using multiple macro cells and associated antennas, Pico cells and microcells further enhance the resolution by providing additional referencepoints.

The embodiment described above of individual range estimates in a signalensemble of multiple copies from multiple antenna can be furtherenhanced by changes to the signal transmit structure in the followingtwo ways. The first is to time multiplex the transmissions from eachantenna. The second approach is to frequency multiplex for each of theantennas. Using both enhancements, time and frequency multiplexingsimultaneously, further improve the ranging and location accuracy of thesystem. Another approach is to add a propagation delay to each antenna.The delay values would be chosen to be large enough to exceed the delayspread in a particular DAS environment (channel), but smaller than theCyclic Prefix (CP) length so that the multipath caused by additionaldelays will not result in ISI (Inter Symbol Interference).

The addition of a unique ID or unique identifier for each antennaincreases the efficiency of the resulting solution. For example, iteliminates the need for the processor to estimate all the ranges fromthe signals from each of the antennas

In one embodiment utilizing the LTE downlink, one or more referencesignal(s) subcarriers, including pilot and or synchronization signal(s)subcarriers, are used to determine subcarriers phase and amplitude thatare in turn applied to the multi-path processor for multipathinterference mitigation and generation of range based locationobservables and locate estimate using multilateration and locationconsistency algorithms to edit out wild points.

Another embodiment takes advantage of the fact that the LTE uplinksignaling also includes reference signals, mobile device to base, whichalso contains reference subcarriers. In fact there is more than one modein which contain these subcarriers from a full sounding mode used by thenetwork to assign a frequency band to the uplink device to a mode whereare reference subcarriers are used to generate a channel impulseresponses to aid in demodulation of the uplink signal, etc. Also,similarly to the DL PRS added in rel.9 additional UL reference signalsmight be added in the upcoming and future standard releases. In thisembodiment, the uplink signal is processed by multiple base units (eNB)using the same range to phase, multipath mitigation processing togenerate range related observables. In this embodiment, locationconsistency algorithms are used as established by the multilaterationalgorithm to edit wild point observables and generate a locationestimate.

Yet another embodiment, relevant one or more reference (including pilotand/or synchronization) subcarriers of both the LTE downlink and LTEuplink are collected, the range to phase mapping is applied, multipathmitigation is applied, and the range associated observable is estimated.These data would then be fused in such a way that would provide a morerobust set of observables for location using the multilaterationalgorithm and location consistency algorithms. The advantage would bethe redundancy that results in improved accuracy since the downlink andup link two different frequency bands or in case of the TDD (TimeDivision Duplexing) improving the system coherency.

In a DAS (Distributed Antenna System) environment where multipleantennas transmitting the same downlink signal from a microcell thelocation consistency algorithm(s) are extended to isolate the ranges ofthe DAS antennas from observables generated by the multipath mitigationprocessing from reference signal(s) (including pilot and/orsynchronization) subcarriers and to obtain the location estimates fromthe multiple DAS emitters (antennas) ranges.

In a DAS system (environment) obtaining accurate location estimate ispossible only if the signals paths from individual antennas can beresolved with a high accuracy, whereby the path error is only a fractionof the distance between antennas (accuracy of 10 meters or better).Because all existing techniques/methods cannot provide such accuracy ina heavy multipath environment (signals from multiple DAS antennas willappear as induced heavy multipath) the existing techniques/methodscannot take advantage of the abovementioned extension of the locationconsistency algorithm(s) and this locate method/technique in the DASenvironment.

The multi-path mitigation methods and systems for object identificationand location finding, described in U.S. Pat. No. 7,872,583, is appliedto the range to signal phase mapping, multipath interference mitigationand process to generate range based location observables utilizing theLTE downlink, uplink and/or both (downlink and uplink), one or morereference signal(s) subcarriers and using multilateration and locationconsistency to generate a location estimate.

In all above embodiments trilateration positioning algorithms can bealso employed.

The DL-OTDOA locating was specified in the LTE release 9: EvolvedUniversal Terrestrial Radio Access (E-UTRA); Physical channels andmodulation; 3GPP TS 36.211 Release 9 technical Specification. However,it has not been implemented by the wireless operators (carriers). In themeantime, a Downlink locating can be implemented within current, e.g.unmodified, LTE network environment by using the existing physical layermeasurements operation(s).

In LTE the UE and the eNB are required to make physical layermeasurements of the radio characteristics. The measurement definitionsare specified in 3GPP TS 36.214. These measurements are performedperiodically and are reported to the higher layers and are used for avariety of purposes including intra- and inter-frequency handover,inter-radio access technology (inter-RAT) handover, timing measurements,and other purposes in support of RRM (Radio Resource Management).

For example, the RSRP (Reference Signal Received Power) is the averageof the power of all resource elements which carry cell-specificreference signals over the entire bandwidth.

Another example is the RSRQ (Reference Signal Received Quality)measurement that provides additional information (RSRQ combines signalstrength as well as interference level).

The LTE network provides the UE with eNB neighbor (to serving eNB)lists. Based on the network knowledge configuration, the (serving)eNodeB provides the UE with neighboring eNB's identifiers, etc. The UEthen measures the signal quality of the neighbors it can receive. The UEreports the results back to the eNodeB. Note: UE also measures thesignal quality of the serving eNB.

According to the specification, the RSRP is defined as the linearaverage over the power contributions (in [W]) of the resource elementsthat carry cell-specific reference signals within the consideredmeasurement frequency bandwidth. The measurement bandwidth that is usedby the UE to determine RSRP is left up to the UE implementation with thelimitation that corresponding measurement accuracy requirements have tobe fulfilled.

Considering the measurement bandwidth accuracy requirements thisbandwidth is fairly large and the cell-specific reference signals thatare used in the RSRP measurements can be further processed to determinethese reference signals subcarriers phase and amplitude that are in turnapplied to the multi-path processor for multipath interferencemitigation and generation of range based location observables. Inaddition, other reference signals that are used in the RSRP measurement,for example SSS (Secondary Synchronization Signal) might be also used.

Thereafter, based on range observables from three or more cells thelocation fix can be estimated using multilateration and locationconsistency algorithms.

As was mentioned previously while there are several causes of the RFfingerprinting database instability one of the major ones is themultipath (the RF signature is very sensitive to multipath). As aresult, the RF Fingerprinting method(s)/technology locate accuracy isheavily impacted by multipath dynamics—changes over time, environment(for example weather), people and/or objects movement, includingvertical uncertainty: >100% variability depending upon device Z-heightand/or antenna orientation (see Tsung-Han Lin, et al. MicroscopicExamination of an RSSI-Signature-Based Indoor Localization System).

The present embodiments can significantly improve the RF Fingerprintinglocate accuracy because of the ability (multipath processor) to find andcharacterize each individual path, including significantly attenuatedDLOS. As a result, the RF Fingerprinting decision on the location fixcan be supplemented with the real-time multipath distributioninformation

As was mentioned above, the locate fix will require position referencessynchronization in time. In wireless networks these position referencesmay include Access Points, Macro/Mini/Pico and Femto cells, as wells asso called Small cells (eNB). However, wireless operators do notimplement the synchronization accuracy that is needed for an accurateposition fix. For example, in case of LTE the standard does not requireany time synchronization between eNB(s) for the FDD (Frequency DivisionDuplexing) networks. For LTE TDD (Time Division Duplexing) this timesynchronization accuracy is limit is +/−1.5 microseconds. This isequivalent to 400+ meters locate uncertainty. Although not required, theLTE FDD networks are also synchronized but use even larger (than 1.5microseconds) limits.

Wireless LTE operators are using GPS/GNSS signals to synchronize eNB(s)in frequency and time. Note: The LTE eNB has to maintain a very accuratecarrier frequency: 0.05 ppm for macro/mini cells and slightly lessaccurate for other type of cells (0.1-0.25 ppm). The GPS/GNSS signalscan also enable a required (for locate) time synchronization accuracy ofbetter than 10 nanoseconds. However, network operators and networkequipment manufacturers are trying to reduce costs associated with theGPS/GNSS units in favor of Packet Transport/, e.g. Internet/Ethernetnetworking time synchronization by employing NTP (Network Time Protocol)and/or PTP (Precision Time Protocol), for example IEEE 1588v2 PTP.

The IP network-based synchronization has a potential of meeting theminimum frequency and time requirements, but is lacking the GPS/GNSSprecision that is needed for locate fix.

The approach described herein is based on the GPS/GNSS signals andsignals generated by the eNB and/or AP, or other wireless networksequipment. It also can be based on the IP networking synchronizationsignals and Protocols and signals generated by the eNB and/or AP, orother wireless networks equipment. This approach is also applicable toother wireless networks, including WiMax, WiFi, and White Space.

The eNB signals are received by the Time Observation Unit (TMO)installed at the operator's eNB facility (FIG. 12). The TMO also includethe External Synchronization Source input.

The eNB signals are processed by the TMO and are time stamped usingclocks that are synchronized with the External Synchronization Sourceinput.

The External Synchronization Source could be from the GPS/GNSS and/orInternet/Ethernet networking, for example PTP or NTP, etc.

The time-stamped processed signal, for example the LTE frame start(could be other signals, especially in other networks), also includesthe eNB (cell) location and/or cell ID, is sent via theInternet/Ethernet backhaul to a central TMO Server that creates,maintains and updates a data base of all eNBs.

The UE and/or eNB(s) involved in the process of ranging and obtaining alocation fix will quire the TMO Server and the server will return thetime synchronization offsets between the eNB(s) involved. These timesynchronization offsets will be used by the UE and/or eNB(s) involved inthe process of obtaining a location fix to adjust the location fix.

Alternatively, the location fix calculations and adjustment can becarried out by the TMO Server when UE and/or eNB(s) involved in theprocess of ranging will also supply the obtained ranging information tothe TMO Server. The TMO Server will then return an accurate (adjusted)position (locate) fix.

If more than one cell eNB equipment is co-located together a single TMOcan process and time stamp signals from all eNB(s).

The RTT (Round Time Trip) measurements (ranging) can be used forlocating. The drawback is that the RTT ranging is subject to multipathwhich has drastic impact on the locate accuracy.

On the other hand, RTT locating does not require the position referencessynchronization (in time) in general and in case of LTE the eNB inparticular.

At the same time, when operating with Pilot Reference and/or othersignals of the wireless network the multipath mitigation processor,method(s)/techniques and algorithms described in U.S. Pat. No. 7,872,583are capable of determining the channel response for the RTT signal(s),e.g. identify the multipath channel that the RTT signal(s) are goingthrough. This allows for correction of the RTT measurements so that theactual DLOS time will be determined.

With DLOS time known it will be possible to obtain the location fixusing trilateration and/or similar locating methods without the need ofeNB or position references synchronization in time.

Even with TMO and TMO Server in place the technology integrationdescribed herein and in related applications will require changes in themacro/mini/pico and small cells and/or UE (cell phone). Although thesechanges are limited only to SW/FW (software/firmware) it takes a lot ofeffort to revamp the existing infrastructure. Also, in some casesnetwork operators and/or UE/cell phone manufacturers/suppliers resistingequipment modifications. Note: UE is wireless network User Equipment.

This SW/FW change can be completely avoided if the TMO and TMO Serverfunctionality is expanded to support the locate technology describedherein and in related applications. In other words, another embodimentdescribed below operates with wireless networks signals, but do notrequire any modifications of the wireless networkequipment/infrastructure. Thus, the embodiment described below operateswith LTE networks and it is also applicable to other wirelesssystems/networks, including Wi-Fi.

In essence this embodiment creates a parallel wireless locateinfrastructure that uses the wireless network signals to obtain locationfix.

Similarly to TMO and TMO Server, the locate infrastructure describedherein and in related applications will consists of one or more wirelessNetwork Signals Acquisition Units (NSAU) and one or more Locate ServerUnits (LSU) that collect data from NSAU(s) and analyze it, determiningrange and locations, and to convert it into a table, e.g. of phone/UEsIDs and locations at an instant of time. The LSU interfaces to thewireless network via network's API.

Multiple of these units could be deployed in various locations in alarge infrastructure. If NSAU(s) have coherent timing—the results forall can be used which will give better accuracy.

The coherent timing can be derived from the GPS clock and/or otherstable clock sources.

The NSAU communicates with LSU via LAN (Local Area Network), Metro AreaNetwork (MAN) and/or Internet.

In some installation/instances the NSAU and LSU could becombined/integrated into a single unit.

In order to support location services using LTE or other wirelessnetworks, the transmitters are required to be clock and eventsynchronized to within tight tolerances. Normally this is accomplishedby locking to the 1 PPS signal of GPS. This will result in timingsynchronization in a local area to within 3 nanosecond 1-sigma.

However, there are many instances when this type of synchronization isnot practical. The present embodiments provide time offset estimatesbetween the downlink transmitters and tracking of the time offsets inorder to provide delay compensation values to the location process sothe location process can proceed as if the transmitters were clock andevent synchronized. This is accomplished by prior knowledge of thetransmit antenna (which is required for any location services) and areceiver with known a priori antenna location. This receiver called thesynchronization unit will collect data from all the downlinktransmitters and given its knowledge of the locations, calculate theoffset timing from a preselected base antenna. These offsets are trackedby the system through the use of a tracking algorithm that compensatesfor clock drifts the downlink transmitters. Note: The processing toderive pseudo ranges from the received data will utilize the Multipathmitigation algorithms (described in U.S. Pat. No. 7,872,583). Hence thesynchronization will not be impacted by multipath.

These offset data are used by the location processor (Location Server,LSU) to properly align the data from each downlink transmitter so thatit appears to have been generated by synchronized transmitters. The timeaccuracy is comparable with the best 1-PPS tracking and will support 3meter location accuracy (1-sigma).

The synchronization receiver and/or receiver's antennas will be locatedbased on optimal GDOP for best performance. In large installationsmultiple synchronization receivers can be utilized to provide anequivalent 3 nsec 1-sigma synchronization offset throughout the network.By utilizing synchronization receivers(s) the requirements forsynchronization of the downlink transmitters is eliminated.

The synchronization receiver unit can be a standalone unit communicatingwith the NSAU and/or LSU. Alternatively, this synchronization receivercan be integrated with the NSAU.

The exemplary wireless network locate equipment diagram is depicted inFIG. 13.

The embodiment of a completely autonomous system, no Customer NetworkInvestment, which utilizes LTE signals operates in the following modes:

1. Uplink mode—uses wireless network Uplink (UL) signals for the purposeof locating (FIGS. 16 and 17)

2. Downlink mode—uses wireless network Downlink (DL) signals for thepurpose of locating (FIGS. 14 and 15).

3. Two-way mode—uses both: UL and DL signals for locating.

In the Uplink mode multiple antennas are connected to one or more NSAUs.These antennae locations are independent from the wireless networkantennas; NSAU(s) antennae locations are selected to minimize the GDOP(Geometric Dilution of Precision).

Network′ RF signals from the UE/cell phone devices are collected byNSAU(s) antennae and are processed by NSAU(s) to produce time stampedsamples of the processed network′ RF signals during a time interval thatis adequate for capturing one or more instances of all signals ofinterest.

Optionally, NSAU will also receive, process and time stamped samples ofDownlink signals to obtain additional information, for example fordetermining UE/phone ID, etc.

From captured time stamped samples the UE/cell phone devicesidentification numbers (ID) together with time stamped wireless networksignals of interest that associated with each UE/cell phone ID(s) willbe determined (obtained). This operation can be performed either by theNSAU or by the LSU.

The NSAU will periodically supply data to the LSU. If unscheduled datais needed for one or more UE/cell phone ID(s) then LSU will requestadditional data.

No changes/modifications will be needed in wireless networkinfrastructure and/or existing UE/cell phone for the UL mode operation.

In the Downlink (DL) mode a location-enabled UE will be required. Also,the cell phone FW would have to be modified if phone is used to obtainlocation fix.

In some instances operators can make baseband signals available fromBBU(s) (Base Band Units). In such cases NSAU(s) will also be capableprocess these available base band wireless network signals instead of RFwireless network signals.

In the DL mode there is no need to associate the UE/cell phone ID withone or more wireless network signals because these signals will beprocessed in the UE/cell phone or UE/cell phone will periodicallyproduce time stamped samples of the processed network′ RF signals andsend these to the LSU; and the LSU will send result(s) back to theUE/cell phone.

In the DL mode the NSAU will process and time stamp processed RF orbaseband (when available) wireless network signals. From captured timestamped samples wireless network signals DL frames starts associatedwith the network antennas will be determined (obtained) and thedifference (offset) between these frame starts will be calculated. Thisoperation can be performed either by the NSAU or by the LSU. Framestarts offsets for network antennas will be stored on the LSU.

In the DL mode frame starts offsets of network antennas will be sentfrom LSU to the UE/phone device in case when the device willprocess/determine its own location fix using technology described hereinand in related applications. Otherwise, when UE/cell phone device willperiodically send time stamped samples of the processed network′ RFsignals to the LSU, the LSU will determine the device's location fix andwill send the location fix data back to the device.

In DL mode the wireless network RF signals will come from one or morewireless network antennae. To avoid multipath impact on results accuracythe RF signal should be sniffed out from the antenna or the antennaconnection to the wireless network equipment.

The two-way mode encompasses determination of the location fix fromboth: UL and DL operations. This allows further improve the locateaccuracy.

Some Enterprise set ups use one or more BBUs feeding one or more RemoteRadio Heads (RRH), with each RRH in turn feeding multiple antennae withthe same ID. In such environments, depending on wireless networkconfiguration, determining the DL mode frame starts offsets of networkantennas might not be required. This includes a single BBU set up aswell as multiple BBUs, whereby antennae of each BBU are assigned to acertain zone and adjacent zones coverages are overlapping.

On the other hand a configuration, configuration whereby antennae thatare fed from multiple BBUs are interleaved in the same zone will requiredetermining the DL mode frame starts offsets of network antennas.

In DL mode of operation in DAS environment multiple antennae may sharethe same ID.

In the present embodiments, location consistency algorithm(s) areextended/developed to isolate the ranges of the DAS antennas fromobservables generated by the multipath mitigation processing fromreference signal(s) (including pilot and/or synchronization) subcarriersand to obtain the location estimates from the multiple DAS emitters(antennas) ranges.

However, these consistency algorithms have limits of number of antennaethat emit the same ID. It is possible to reduce the number of antennaethat emit the same ID by

1. For a given coverage zone interleave Antennas that are fed fromdifferent sectors of sectorized BBU (BBUs are capable of supporting upto six sectors)

2. For a given coverage zone interleave Antennas that are fed fromdifferent sectors of sectorized BBU as well as Antennas that are fedfrom different BBUs

3. Adding a propagation delay element to each antenna. The delay valueswould be chosen to be large enough to exceed the delay spread in aparticular DAS environment (channel), but smaller than the Cyclic Prefix(CP) length so that the multipath caused by additional delays will notresult in ISI (Inter Symbol Interference). The addition of a uniquedelay ID for one or more antenna further reduces the number of antennaethat emit the same ID.

In an embodiment, an autonomous system with no Customer NetworkInvestment can be offered. In such embodiment, the system can operate ona band other than the LTE band. For example, ISM (industrial Scientificand Medical) bands and/or White Space bands can be used in places whereLTE services are not available.

The embodiment can be also integrated with the macro/mini/pico/femtostation (s) and/or UE (cell phone) equipment. Although the integrationmay require Customer Network Investment, it can reduce cost overhead andcan dramatically improve the TCO (Total Cost of Ownership).

As mentioned herein above, PRS can be used by the UE for the DownlinkObserved Time Difference of Arrival (DL-OTDOA) positioning. Regardingthe synchronization of neighboring base stations (eNBs), the 3GPP TS36.305 (Stage 2 functional specification of User Equipment (UE)positioning in E-UTRAN) specifies transferring timing to the UE, thetiming being relative to an eNode B service of candidate cells (e.g.,neighboring cells). The 3GPP TS 36.305 also specifies Physical cell IDs(PCIs) and global cell IDs (GCIs) of candidate cells for measurementpurposes.

According to the 3GPP TS 36.305, this information is delivered from theE-MLC (Enhanced Serving Mobile Location Centre) server. It is to benoted that the TS 36.305 does not specify the abovementioned timingaccuracy.

Additionally, the 3GPP TS 36.305 specifies that the UE shall return tothe E-MLC the downlink measurements, which includes Reference SignalTime Difference (RSTD) measurements.

The RSTD is the measurement taken between a pair of eNBs (see TS 36.214Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layermeasurements; Release 9). The measurement is defined as a relativetiming difference between a subframe received from the neighboring cellj and a corresponding subframe of the serving cell i. PositioningReference Signals are used to take these measurements. The results arereported back to the location server that calculates the position.

In an embodiment, a hybrid method can be defined to accommodate both thenewly introduced PRS and the already existing reference signals. Inother words, the hybrid method can use/operate with PRS, with otherreference signals (e.g., cell or node-specific reference signals (CRS)),or with both signal types.

Such a hybrid method provides the advantage of allowing networkoperator(s) to dynamically choose the mode of operation depending oncircumstances or network parameters. For example, the PRS have betterhearability than CRS, but may result in up to 7% reduction in the datathroughput. On the other hand, CRS signals do not cause any throughputreduction. In addition, CRS signals are backward compatible with allprevious LTE releases, for example Rel-8 and lower. As such, the hybridmethod provides a network operator the ability to trade-off or balancebetween hearability, throughput, and compatibility.

In Long Term Evolution (LTE) implementations, LTE downlink basebandsignals (generated by a cell or wireless node and referred to herein as“nodes”) are generally combined into downlink frames. A receiver fordetecting and receiving such signals may detect downlink frames frommultiple cells or nodes (two or more). Each downlink frame includesmultiple CRS or reference signals. In a Downlink (DL) frame, thesereference signals have predetermined positions in time and frequency,e.g., there are deterministic time offsets between the frame start andeach CRS in a given frame.

In addition, each CRS is modulated with a special code. The modulationand the code are also predetermined. The CRS modulation is the same forall nodes, but the code (seed) is determined by the ID (identification)number of the node.

As a result, by knowing the node ID(s), it is possible to estimate acourse location of a frame start time for each frame from each node(cell), in the spectrum of the reference signals. To do so, it is firstnecessary to determine the frame start times or frame starts for all DLsignals from different nodes. For example, in an embodiment, bycorrelating the received DL baseband signal with known replicas of codemodulated CRS (generated internally by a detector and/or a multipathmitigation processor) it is possible to find all CRS sequences or otherreference signals from various nodes, and with this information findcoarse location frame starts of all observable nodes. In an embodiment,the detector may also demodulate/decode the CRS and then correlate thedemodulated/decoded CRS with baseband sub-carriers that are assigned tothe CRS.

At the same time, in an embodiment, the CRS may also be used as rangingsignals by the multipath mitigation processor. Therefore, in addition tofinding coarse frame starts the detector's correlation process is alsocapable of isolating the CRS from other signals (such as payload) in theframe using the code that was used to modulate those signals.Thereafter, these isolated CRS, and associated frames starts, aretransferred to a multipath mitigation processor for ranging.

A similar approach can be used in the Uplink mode, whereby timingoffsets between different node receivers can be determined.

In a downlink embodiment, a system for tracking and locating one or morewireless network devices in communication with a network comprises auser equipment receiver configured to receive multiple signals from twoor more nodes in communication with the network, the multiple signalsbeing modulated with a code determined by an identification of each nodeof the two or more nodes transmitting the multiple signals, the userequipment receiver including a detector configured to detect and isolatereference signals from the multiple signals based on the identification,and a processor configured to use the reference signals as rangingsignals from each node for tracking and locating the one or morewireless network devices.

In the embodiment, wherein the multiple signals from each node of thetwo or more nodes are combined into a frame that includes the referencesignals, and wherein the detector is further configured to estimate acourse location of frame starts from each node.

In the embodiment, wherein the detector is further configured toestimate the course location by correlating the reference signals withknown replicas of such reference signals.

In the embodiment, wherein the detector is further configured to isolatethe reference signals from any other signals in the frame, and whereinthe detector is further configured to isolate the reference signals foreach node of the two or more nodes.

In the embodiment, wherein the processor is at least one multipathmitigation processor, and wherein the multipath mitigation processor isconfigured to receive the course location and isolated reference signalsand estimate a relative time of arrival of the ranging signals from eachnode.

In the embodiment, wherein the processor is at least one multipathmitigation processor.

In the embodiment, wherein the multiple signals from each node of thetwo or more nodes are in a frame, wherein the detector is furtherconfigured to estimate a course location of frame starts from each node,wherein the detector is configured to isolate the reference signals fromany other signals in the frame, wherein the detector is furtherconfigured to isolate the reference signals for each node of the two ormore nodes, wherein the detector is configured to pass the courselocation and isolated reference signals for each node to the multipathmitigation processor, and wherein the multipath mitigation processor isconfigured to receive the course location and isolated reference signalsand estimate a relative time of arrival of the ranging signals from eachnode.

In the embodiment, the system further comprises an uplink embodimentwhere a node receiver is configured to receive device signals from theone or more wireless network devices, the device signals being modulatedwith a device code determined by a device identification of eachwireless network device of the one or more wireless network devicestransmitting the device signals, the node receiver including a devicedetector configured to detect and isolate device reference signals fromthe device signals based on the device identification, and a secondprocessor is configured to use the device reference signals as rangingsignals from each wireless network device for tracking and locating theone or more wireless network devices.

In an embodiment, a system for tracking and locating one or morewireless network devices in communication with a network, comprises auser equipment receiver configured to receive multiple signals from twoor more nodes in communication with the network, the multiple signalsbeing modulated with a code determined by an identification of each nodeof the two or more nodes transmitting the multiple signals, and aprocessor configured to detect and isolate reference signals from themultiple signals based on the identification and to use the referencesignals as ranging signals from each node for tracking and locating theone or more wireless network devices.

In the embodiment, wherein the multiple signals from each node of thetwo or more nodes are combined into a frame that includes the referencesignals, and wherein the processor is further configured to estimate acourse location of frame starts from each node.

In the embodiment, wherein the processor is further configured toestimate the course location by correlating the reference signals withknown replicas of the reference signals.

In the embodiment, wherein the processor is further configured toestimate a relative time of arrival of the ranging signals from eachnode based on the course location and isolated reference signals.

In the embodiment, wherein the processor is further configured toisolate the reference signals from any other signals in the frame, andwherein the processor is further configured to isolate the referencesignals for each node of the two or more nodes.

In the embodiment, wherein the multiple signals from each node of thetwo or more nodes are in a frame, wherein the processor is furtherconfigured to estimate a course location of frame starts from each nodeby correlating the reference signals with known replicas of thereference signals, wherein the processor is further configured toisolate the reference signals from any other signals in the frame and toisolate the reference signals for each node of the two or more nodes,and wherein the processor is further configured to estimate a relativetime of arrival of the ranging signals from each node based on thecourse location and isolated reference signals.

In an embodiment, a system for tracking and locating one or morewireless network devices in communication with a network, comprises adetector configured to receive multiple signals from two or more nodesin communication with the network, the multiple signals being modulatedwith a code determined by an identification of each node of the two ormore nodes transmitting the multiple signals, and to detect and isolatereference signals from the multiple signals based on the identification,and a processor configured to use the reference signals as rangingsignals from each node for tracking and locating the one or morewireless network devices.

In the embodiment, wherein the multiple signals from each node of thetwo or more nodes are combined into a frame that includes the referencesignals, and wherein the detector is further configured to estimate acourse location of frame starts from each node.

In the embodiment, wherein the detector is further configured toestimate the course location by correlating the reference signals withknown replicas of such reference signals.

In the embodiment, wherein the detector is further configured to isolatethe reference signals from any other signals in the frame, and whereinthe detector is further configured to isolate the reference signals foreach node of the two or more nodes.

In the embodiment, wherein the processor is at least one multipathmitigation processor, and wherein the multipath mitigation processor isconfigured to receive the course location and isolated reference signalsand estimate a relative time of arrival of the ranging signals from eachnode.

In the embodiment, wherein the processor is at least one multipathmitigation processor.

In the embodiment, wherein the multiple signals from each node of thetwo or more nodes are in a frame, wherein the detector is furtherconfigured to estimate a course location of frame starts from each node,wherein the detector is configured to isolate the reference signals fromany other signals in the frame, wherein the detector is furtherconfigured to isolate the reference signals for each node of the two ormore nodes, wherein the detector is configured to pass the courselocation and isolated reference signals for each node to the multipathmitigation processor, and wherein the multipath mitigation processor isconfigured to receive the course location and isolated reference signalsand estimate a relative time of arrival of the ranging signals from eachnode.

In an embodiment, a system for tracking and locating one or morewireless devices in communication with a network, comprises a nodereceiver configured to receive device signals from the one or morewireless network devices, the device signals being modulated with adevice code determined by a device identification of each wirelessnetwork device of the one or more wireless network devices transmittingthe device signals, the node receiver including a device detectorconfigured to detect and isolate device reference signals from thedevice signals based on the device identification, and a processorconfigured to use the device reference signals as ranging signals fromeach wireless network device for tracking and locating the one or morewireless network devices.

Furthermore, the hybrid method can be transparent to the LTE UEpositioning architecture. For instance, the hybrid method can operate inthe 3GPP TS 36.305 framework.

In an embodiment, RSTD can be measured and, according to the 3GPP TS36.305, transferred from a UE to an E-SMLC.

The UL-TDOA (U-TDOA) is currently in a study phase and is expected to bestandardized in the upcoming release 11.

Embodiments of the UL-TDOA (Uplink) are described herein above and arealso shown in FIGS. 16 and 17. FIGS. 18 and 19, described herein below,provide examples of alternative embodiments of the UL-TDOA.

FIG. 18 presents an environment that may include one or more DAS and/orFemto/Small cell antennas. In this example embodiment, each NSAU isequipped with a single antenna. As depicted, at least three NSAUs arerequired. However, additional NSAUs can be added to improve hearabilitybecause each UE must be “heard” by at least three NSAUs.

Furthermore, the NSAU(s) can be configured as receivers. For example,each NSAU receives but does not transmit information over the air. Inoperation, each NSAU can listen to the wireless Uplink network signalsfrom UEs. Each of the UEs can be a cell phone, a Tag, and/or another UEdevice.

Moreover, the NSAUs can be configured to communicate with a LocateServer Unit (LSU) over an interface, such as a wired service or a LAN.In turn, the LSU can communicate with a wireless or an LTE network. Thecommunication can be via a network API, where the LSU can, for example,communicate with an E-SMLC of the LTE network and can use a wiredservice such as a LAN and/or a WAN.

Optionally, the LSU may also communicate directly with DAS basestation(s) and or Femto/Small cells. This communication can use the sameor a modified Network API.

In this embodiment, the Sounding Reference Signal (SRS) can be used forlocate purposes. However, other signals may also be employed.

The NSAUs can convert the UE Uplink transmission signals to a digitalformat, for example I/Q samples, and can periodically send a number ofthe converted signals to the LSU with a time stamp.

The DAS base station(s) and or Femto/Small cells can pass to the LSU oneor all of the following data:

1) the SRS, the I/Q samples, and the time stamp;

2) a list of served UE IDs; and

3) SRS schedule per UE with a UE ID, the schedule including SRSSchedulingRequestConfig information and SRS-UL-Config information.

The information passed to the LSU may not be limited by theabovementioned information. It can include any information needed tocorrelate each UE device uplink signal, such as a UE SRS, with each UEID.

The LSU functionality can include ranging calculations and obtaining thelocation fix of a UE. These determinations/calculations can be based onthe information passed from the NSAUs, the DAS bases stations, and/orFemto/Small cells to the LSU.

The LSU may also determine timing offsets from the available downlinktransmission information passed from the NSAUs to the LSU.

In turn, the LSU can provide the wireless or LTE network with UElocation fix and other calculations and data. Such information can becommunicated via the Network API.

For synchronization purposes, each NSAU may receive, process, and timestamp samples of Downlink signals. Each NSAU may also periodically senda number of such samples to the LSU, including the time stamp(s).

Additionally, each NSAU may include an input configured forsynchronization with external signal(s).

FIG. 19 depicts another embodiment of a UL-TDOA. In addition to thecomponents depicted under FIG. 18, the environment of this embodimentmay include one or more cell towers that can be used in lieu of the DASbase stations and/or Femto/Small cells. Data from the one or more celltowers can be used to obtain the location fix of a UE.

As such, an advantage of this embodiment includes obtaining a locationfix with only a single cell tower (eNB). In addition, this embodimentcan be configured to operate in a similar manner as described under FIG.18, with the exception that one or more eNBs can replace the DAS basestations and/or the Femto/Small cells.

One method of uplink locating of UE is the Cell Identification method(CID). In the basic CID method, the UE position may be determined on thecell level. This method is purely network based. As a result, the UE,for example a handset, is not aware of the fact that it is beingtracked. While this is a relatively simple method, it lacks accuracybecause the locate uncertainty is equal to the cell diameter. Forexample, as illustrated in FIG. 20, any of the handsets 2000 within thecell diameter 2002 of a serving cell tower 2004 effectively have thesame location, even though they are not at the same location. Theaccuracy of the CID method can be improved when combined with servingsector identification (sector ID) knowledge. For example, as illustratedin FIG. 21, sector ID 2100 identifies a section 2102 within the celldiameter 2002 that includes a number of handsets 2104 that are known tohave a different location than the other handsets 2000 in other sectorsof the cell diameter 2002.

Further enhancement to the CID method may be possible through theEnhanced Cell ID (E-CID) method, which provides further refinements tothe basic CID method described above. One enhancement uses timingmeasurements to calculate how far away the UE is from the eNB (thenetwork node). This distance can be calculated as half the round triptime (RTT), or Timing Advance (TA) in LTE (LTE TA), times the speed oflight. If the UE is connected, then RTT or TA may be used for distanceestimation. In this case both: the serving cell tower or sector and theUE (upon the serving eNB command) will measure the timing differencebetween Rx sub-frames and Tx sub-frames. The UE will report itsmeasurements to the eNB (also under the eNB control). It should be notedthat LTE Rel-9 adds the TA type 2 measurements that rely on the timingadvance estimated from receiving a PRACH preamble during the randomaccess procedure. A PRACH (physical/packet random access channel)preamble specifies the maximum number of preambles to be sent during onePRACH ramping cycle when no response is received from the UE beingtracked. The LTE Type 1 TA measurement is the equivalent to the RTTmeasurement, as follows:RTT=TA(type 1)=eNB(Rx-Tx)+UE(Rx-Tx)With knowledge of the eNB's coordinates and the height of the servingcell tower antenna, the position of the UE can be calculated by thenetwork.

The E-CID locating method is still limited, however, because in onedimension the locate accuracy depends upon the sector width and thedistance from the serving cell tower, and in the other dimension theerror depends upon the TA (RTT) measurement accuracy. The sector widthvaries with network topology and is impacted by the propagationphenomena, specifically multipath. Sector accuracy estimates vary from200 meters to in excess of 500 meters. The LTE TA measurement resolutionis 4 Ts, which corresponds to 39 meters of maximum error. The actualerror in the LTE TA measurement is even larger, however, due tocalibration inaccuracies and the propagation phenomena (multipath), andmay reach as much as 200 meters.

As illustrated in FIG. 22 the E-CID method may be further improved withthe addition of a feature known as Angle of Arrival (AoA). The eNBestimates the direction from which the UE is transmitting using a lineararray of equally spaced antenna elements 2200. Typically, referencesignals are used for the AoA determination. When reference signals arereceived from the UE at two adjacent antenna elements 2200, thereference signals may be phase rotated, as shown in FIG. 23 by an amountwhich depends on the AoA, the carrier frequency, and the elementspacing. The AoA will require each eNB to be equipped with antennaarrays/adaptive antennas. It is also exposed to multipath and topologyvariances. Nevertheless, sophisticated antenna arrays can significantlyreduce the width 2202 of the sector 2100, which may lead to betterlocate accuracy. Moreover, if two or more serving cell towers 2300(eNB's base stations equipped with directional antenna arrays) can beused to make the handset AoA determination, as illustrated in FIG. 23then the accuracy may be considerably improved. In such a case, theaccuracy is still subject to the multipath/propagation phenomena.

Deploying antenna arrays/adaptive antennas network-wide over multipleLTE bands requires a monumental effort in terms of capital, time,maintenance, etc. As a result, the antenna arrays/adaptive antennas havenot been deployed for the purpose of UE locating. Other approaches, suchas signal strength based methods, do not produce significant accuracyimprovement. One such signal strength approach is fingerprinting, whichrequires creating and continuously updating an enormous, continuouslychanging (in time) fingerprint database, e.g. large capital andreoccurring expenses without significant accuracy improvement. Moreover,fingerprinting is UE based technology, whereby the UE position cannot bedetermined without UE assistance on the UE application level.

A solution to the limitations of other uplink location methods involvesthe use of AoA capabilities without the need for antenna arrays/adaptiveantennas. Such an embodiment may employ TDOA (Time Difference ofArrival) location techniques for AoA determination, which may be basedon estimating the difference in the arrival times of the signal from thesource at multiple receivers. A particular value of the time differenceestimate defines a hyperbola between two receivers in communication witha UE. When the distance between the receiving antennas is small relativeto the distance of the emitter (the handset) being located, then theTDOA is equivalent to the angle between the baseline of the sensors(receivers antennas) and the incident RF energy from the emitter. If theangle between the baseline and true North is known, then the line ofbearing (LOB) and/or AoA can be determined.

While general locate methods that use either TDOA or LOB (also known asAoA) are known, TDOA locate methods have not been used to determine LOBbecause the TDOA reference points are too close to one another to makethe accuracy of such a technique acceptable. Rather, LOB is usuallydetermined using directional antennas and/or beam-forming antennas. Thesuper resolution methods described herein, however, make it possible touse TDOA for LOB determination while dramatically improving accuracy. Inaddition, without the reference signal processing techniques describedherein, it may not be possible to “hear”, e.g. detect, reference signalscoming from a UE outside of the serving sectors, e.g. by the non-servingsectors and/or antennas. Without the resolution and processingcapabilities described herein, it may not be possible to employ TDOA forLOB determination because at least two points of reference are needed,e.g. two or more sectors and/or antennas). Similarly, a UE may not beable to detect reference signals coming to the UE from other thanserving sectors, e.g. from the non-serving sectors and/or antennas.

For example, in FIG. 24 two antenna separation scenarios areillustrated: wide separation and close (small) separation. In bothscenarios the hyperbola 2400 and the incident line 2402 are crossing atthe handset 2000 location, but in the case of where the antenna 2404separation is wide, this happens at a steeper angle, which in turnsubstantially reduces the locate error. At the same time, in case of theantennas 2404 being close to each other the hyperbola 2400 becomesinterchangeable with the line 2402 of the RF energy incident or theLOB/AoA.

The formula set forth below can be used to determine the incident RFenergy from the emitter, where the time difference in arrival time of RFenergy between two antennas (sensors) is given by:

${\Delta\; t} = \frac{{x\;\sin\;\Theta}\;}{c}$

where:

Δt is the time difference in seconds;

x is the distance between the two sensors in meters;

Θ is the angle between the baseline of the sensors and the incident RFwave, in degrees;

and

c is the speed of light.

Several locate strategies are available through use of the TDOA locatingembodiment, including: (1) when the TDOA measurements (multilateration)between two or more serving cells are available, e.g., wide separation;(2) when the TDOA measurements are only from two or more sectors at oneor more serving cells, e.g., small antenna separations, such LOB/AoA;(3) a combination of strategies (2) and (3); and (4) a combination of TAmeasurements and strategies (1)-(3), e.g., improved E-CID.

As further explained below, in the case of closely positioned antennas,the TDOA locating embodiment may use a line of bearing when the signalsfrom two or more antennas are from the same cell tower. These signalscan be detected in the received composite signal. By knowing the towerlocation and the azimuth of each sector and/or antenna, the line ofbearing and/or AoA can be calculated and utilized in the locationprocess. The LOB/AoA accuracy may be impacted by multipath, noise (SNR),etc., However, this impact may be mitigated by advanced signalprocessing and the multipath mitigation processing techniques describedabove, which may be based on super resolution technology. Such advancedsignal processing includes, but is not limited to, signalcorrelation/correlating, filtering, averaging, synchronous averaging andother methods/techniques.

The serving cell tower 2500 typically consists of multiple sectors, asillustrated in FIG. 25 which shows a three sector (Sector A, Sector Band Sector C) configuration. The three sector deployment illustrated mayinclude one or more antennas 2502 per sector. A single sector, such assector A, may be in control of the UE (handset) because the handsettransmissions will be in Sector A's main lobe (the main lobe's centercoincides with the sector azimuth). At the same time the handsettransmissions will fall outside Sectors B's and C's main lobes, e.g.,into antennas side lobes. Thus, the handset signals will still bepresent in the output signal spectrums of Sectors B and C, but will besignificantly attenuated relative to signals from other handset(s) thatare located in Sector B's or Sector C's main lobes. Nevertheless,through the use of advanced signal processing, as described above andbelow, it is possible to obtain sufficient processing gain on rangingsignals to make them detectable from the neighboring sectors' sidelobes, such as the Sector B and Sector C side lobes. For network-basedlocating purposes, the LTE Uplink SRS (Sounding Reference Signals) maybe employed as ranging signals.

In other words, while the UE uplink reference signal might be in theside lobe of the neighboring sector(s) antennas, the processing gainthrough reference signal processing methods described herein may besufficient to allow a calculation of TDOA between the two (or more)sector antennas. The accuracy of this embodiment may be significantlyenhanced by the multipath mitigation processing algorithms describedabove. Thus, LOB/AOA intersected with the annulus calculated by the LTETA timing may provide a UE location to within an error ellipse ofapproximately 20 meters by 100 meters.

Further locate error reduction may be achieved when the UE can be heardby two or more LTE towers, which is highly probable with the processinggains and multipath mitigation technology described above, In such acase, the intersection of the TDOA hyperbola and one or more LOB/AoAlines may result in a 30 by 20 meter error ellipse (for a two sectorcell tower). If each cell tower supports three or more sectors, then theerror ellipse may be further reduced down to 10-15 meters. If the UE isheard by three or more eNB's (cell towers), then 5 to 10 meters accuracymay be achieved. In high value areas, such as malls, office parks andthe like, additional small cells or passive listening devices may beused to create the necessary coverage.

As was mentioned, above each sector of the cell tower 2500 may includeone or more antennas 2502. In a typical installation, for a givensector, signals from each antenna are combined at the sector's receiverinput. As a result, for locate purposes, two or more sector antennas canbe viewed as a single antenna with composite directionality pattern,azimuth and elevation. The hypothetical antenna composite directionalityand its (main lobe) azimuth and elevation may also be assigned to thesector itself.

In an embodiment, the received signals (in a digital format) from allsectors of each serving cell tower and neighboring serving cell towersare sent to a locate server unit (LSU) for location determination. Also,SRS schedules and TA measurements per each served UE is provided to theLSU by each serving sector from each serving cell tower. Assuming thateach serving cell tower and each neighboring cell tower locationcoordinates, the number of sectors per tower with each hypothetical(composite) sector antenna azimuth and elevation, and each sectorposition at the cell tower are known, the LSU may determine each UEposition relative to the serving cell tower and/or neighboring celltowers. All of the abovementioned information may be sent through wirednetworks, for example LAN, WAN, etc., using one or more standardized orproprietary interfaces. The LSU may also interface the wireless networkinfrastructure using a standardized interface and/or a network carrier'sdefined interface/API. The location determination may also be splitbetween the network node and the LSU or performed solely in the networknode.

In an embodiment, the location determination may be performed in the UEor split between the UE and LSU or network node. In such cases, the UEmay communicate over the air using standard networkingprotocols/interfaces. In addition, the location determination can beperformed through a combination of the UE, the LSU and/or network nodes,or the LSU functionality can be implemented (embedded) into a SUPLserver, a E-SMLC server, and/or a LCS (LoCation Services) system thatcan then be used in place of the LSU.

Embodiments of a Downlink (DL) locate method are reciprocals to theUplink (UL) locate embodiments described above. In a DL embodiment, asector may become a transmitter with a transmit pattern, azimuth andelevation that matches the sector's received directionality, azimuth andelevation. Unlike the uplink embodiments, in DL embodiments, the UEtypically has a single receive antenna. Thus, for UE there is no sensorsbaseline that can be used to determine the RF wave incident. However,the UE can determine the TDOA(s) between different sectors andconsequently a hyperbola(s) (multilateration) between sectors, andbecause the same cell tower sectors are close to each other thehyperbola becomes interchangeable with the line of the RF energyincident or the LOB/AoA, as described above with reference to FIG. 24While the LOB/AoA accuracy may be impacted by multipath, noise (SNR),etc., this impact may be mitigated through use of the advanced signalprocessing and the multipath mitigation processing, which is based onthe super resolution technology, described above.

As noted, UE DL locating can be accomplished in ways that are similar tothe UE uplink locating, with the exception of that the RF wave incidentangle cannot be determined from the formula above. Instead, themultilateration technique may be used for determining the LOB/AoA foreach serving cell tower.

UE DL locate embodiments also employ reference signals. In the DL case,one approach for such network-based locating may be to employ the LTECell-Specific Reference Signals (CRS) as ranging signals. Also, PositionReference Signals (PRS) introduced in LTE Release 9 may be used. Thus,locate may be done using CRS only, PRS only, or both CRS and PRS.

As with UE uplink locate embodiments, for UE downlink locateembodiments, a snap-shot of the UE received signal in digital format maybe sent to the LSU for processing. The UE may also obtain the TAmeasurements and provide those to the LSU. Optionally, TA measurementsper each served UE may be provided to the LSU by each serving sectorfrom each serving cell tower (network node). As previously noted,assuming that each serving cell tower and each neighboring cell towerlocation coordinates, the number of sectors per tower with each sectortransmit pattern azimuth and elevation, and each sector position at thetower are known, the LSU may determine each UE position relative to theserving cell tower and/or neighboring cell towers. In embodiments, thelocation determination may be performed in the UE or split between theUE and LSU or network node. In embodiments, all location determinationscan be performed in the LSU or the network node or split between thetwo.

The UE will communicate/receive measurements results and otherinformation over the air using standard wireless protocols/interfaces.The information exchange between the LSU and network node(s) may bethrough wired networks, for example LAN, WAN, etc., using proprietaryand/or one or more standardized interfaces. The LSU may interface thewireless network infrastructure using a standardized interface and/ornetwork carrier's defined interface/API. The location determination mayalso be split between the network node and the LSU or performed solelyin the network node.

For the UE DL location embodiments described above, antenna port mappinginformation can also be used to determine location. The 3GPP TS 36.211LTE standard defines antenna ports for the DL. Separate referencesignals (pilot signals) are defined in the LTE standard for each antennaport. Thus, the DL signals also carry the antenna port information. Thisinformation is included in the PDSCH (Physical Downlink Shared Channel).The PDSCH uses the following antenna ports: 0; 0 and 1; 0, 1, 2 and 3);or 5. These logical antenna ports are assigned (mapped) to the physicaltransmit antennas, as illustrated in FIG. 26 As a result, this antennaport information can be used for the antenna identification (antennaID).

For example, the antenna port mapping information can be used todetermine the RF wave incident and the hyperbola(s) (multilateration)between antennas (assuming that the antennas locations are known).Depending upon where the location determination is performed, theantenna mapping information must be available to the LSU or UE, ornetwork node. It should be noted that antenna ports are indicated byplacing CRS signals in different time slots and different resourceelements. Only one CRS signal is transmitted per DL antenna port.

In the event of MIMO (Multiple Input Multiple Outputs) deployment in theeNB or network node, receiver(s) may be able to determine the timedifferences of arrivals from a given UE. With knowledge of antennas tothe receiver(s) mapping, e.g. MIMO mapping, including antennaslocations, it may also be possible to determine the RF wave incident(LOB/AoA) to antennas and the hyperbola(s) (multilateration) for giveneNB antennas. Likewise, at the UE, the UE receiver(s) may be able todetermine the time differences of arrival(s) from two or more eNB ornetwork node, and MIMO antennas. With knowledge of the eNB antennalocations and antennas mapping, it will be possible to determine the RFwave incident (LOB/AoA) from antennas and the hyperbola(s)(multilateration) for given eNB antennas. Depending on where thelocation determination is performed; the antenna mapping information hasto be available to the LSU or UE, or network node.

There are other configurations that are subsets of MIMO, such as SingleInput Multiple Outputs (SIMO), Single Output Multiple Inputs (SOMI),Single Input Single Output (SISO), etc. All of these configurations maybe defined/determined by the antenna ports mapping and/or MIMO antennamapping information for locate purposes.

In an aspect, the present embodiments relate to methods and systems forRF-based identification, tracking, and locating of objects, includingRTLS. According to one embodiment, the methods and systems employgeographically distributed clusters of receivers and/or transmittersthat are precisely synchronized in time, e.g., within 10 ns or better,within each cluster, while the inter-cluster time synchronization can bemuch less accurate or not required at all. While a precisesynchronization time of 10 ns or better is described with respect to oneparticular embodiment, it is important to note that the predeterminedsynchronization time required to achieve an accurate location depends onthe equipment being utilized. For example, for some wireless systemequipment, where an accuracy of 3 m is required for an accurate locationdetermination, the predetermined time may need to be 10 ns or better,but with other wireless system equipment, a location accuracy of 50 mmay be more than sufficient. Hence, the predetermined time is based onthe desired accuracy location for the wireless system. The disclosedmethods and systems are a significant improvement to the existingimplementation of tracking and location DL-OTDOA and U-TDOA techniques,which rely on geographically distributed standalone (individual)transmitters and/or receivers.

For example, in the DL-OTDOA technique, the relative timing differencebetween signals coming from neighboring base stations (eNB) iscalculated and the UE position can be estimated in the network with theUE (handset) with or without UE assistance or in the UE (handset) withnetwork assistance (control plane or user plane with SUPL based only) orwithout the network assistance. In DL-OTDOA, once the signals from threeor more base stations are received, the UE measures the relative timingdifference between signals coming from a pair of base stations andproduces hyperbolic lines of position (LOPs). At least three referencepoints (base stations not belonging to a straight line) are needed todefine two hyperbolas. The location (position fix) of the UE is in theintersection of these two hyperbolas (see FIG. 11). The UE position fixis relative to the base stations' RF emitters' (antennas) locations. Asan example, when using the LPP (LTE Positioning Protocol, Rel-9) theDL-OTDOA locating is UE assisted and the E-SMLC (Evolved Serving MobileLocation Centre) is server based.

The U-TDOA technique is similar to the DL-OTDOA, but the roles arereversed. Here, the neighboring Location Management Unit (LMU)calculates the Relative Time of Arrival of the uplink signal coming fromthe UE (handset) and the UE position can be estimated in the networkwithout the UE assistance. Thus, the U-TDOA is LMU assisted and theE-SMLC (Evolved Serving Mobile Location Centre) is server based. Oncethe Relative Time of Arrival values from three or more LMUs areavailable, the network's E-SMLC server produces hyperbolic lines ofposition (LOPs) and the location (position fix) of the UE (see FIG. 27).The UE position fix is relative to the LMUs antennas locations. In anaspect, unlike the DL-OTDOA, the eNB's (base station's) timesynchronization in case of U-TDOA is not necessary—only the LMU(s) willneed precision time synchronization for locating purposes. As anexample, the LMU is essentially a receiver with computing capabilities.As a further example, the LMU receiver employs the SDR (Software DefinedRadio) technology. In a further example, the LMU may be a small cell,macro cell or a special purpose small cell type device that onlyreceives.

Regardless of the implementation, correlating the location of the SRSfor the specific UE, as provisioned by the network, will enableidentification and location of the UE. Location of the SRS may be doneat the network level or within a local sector, such as a DAS for abuilding, a small cell or combination of small cells and macro cellsthat serve a specific area. If the location of the SRS for the UE is notknown a priori, the solution may be able to correlate the UE's locationthrough the covered area. Doing so will show the location history ofwhere the UE has traveled. In some circumstances, it may be desirable todetermine the location of the UE, even if the network does not providean indication of where the SRS is located for a particular UE. Thelocation of the UE may be correlated with the SRS by determining thelocation or proximity of the UE to a known point, thereby correlatingthe UE with the SRS it is transmitting. Such location can beaccomplished through other location/proximity solutions, such as Wi-Fiand Bluetooth. The user may also identify their location via a UEapplication or by walking over to a predetermined location in order toidentify their UE to a location solution.

In FIGS. 11 and 27 only the macro base stations are shown. Also, FIG. 27depicts the LMUs being co-located with the base stations. Thesedepictions are valid options, but the LTE standards do not specify wherethe LMUs can be placed, as long as LMUs placement satisfies themultilateration/trilateration requirements.

In an aspect, a common deployment for indoor environments is DAS(Distributed Antenna System) and/or small cells, which are inexpensivebase stations highly integrated with the RF. The LMU(s) can be placedindoors and/or within a campus-type environment as well, e.g. the U-TDOAcan be used in a DAS and/or small cell environment. In another aspect,the U-TDOA based accurate indoors locating can be achieved with acombination of LMUs positioned indoors and macro cells that arepositioned outside, e.g. without the need of deploying DAS and/or smallcells; or have a reduced number of the small cells. Thus, the LMUs canbe deployed with or without DAS and/or small cells being present. In afurther aspect, the LMUs can be placed in environments where cellularsignal amplifiers/boosters are used; with or without DAS and/or smallcells being present.

The LTE release 11 also contemplates the integration of the LMU and theeNB into a single unit. This, however, will put additional burden on thetime synchronization requirements between small cells if individualsmall cells eNBs are geographically distributed, which wireless/cellularservice providers are not ready to meet, especially indoors and/or inother GPS/GNSS denied environments.

DAS systems are inherently time synchronized to a much higher degree(precision) than geographically distributed macro/mini/small cell/LMUs.Using a DL-DTOA solution in a DAS environment will alleviate the timesynchronization issue, but in a DAS environment, a single base stationserves a large number of distributed antennas, such that multipleantennas are transmitting the same downlink signal with the same cell ID(identification number). As a result, the traditional DL-OTDOA approachfails because there are no identifiable neighboring cells (antennas)generating signals with a different ID. Nevertheless, it is possible touse the DL-OTDOA technique when employing a multi-path mitigationprocessor and multi-path mitigation techniques/algorithms, as describedin U.S. Pat. No. 7,872,583, and extending the use of locationconsistency algorithm(s), as described in U.S. Nonprovisionalapplication Ser. No. 13/566,993, filed Aug. 3, 2012, entitled MULTI-PATHMITIGATION IN RANGEFINDING AND TRACKING OBJECTS USING REDUCEDATTENUATION RF TECHNOLOGY; which are incorporated herein by reference intheir entirety. However, these consistency algorithms have limits of thenumber of antennae that emit signal(s) with the same ID. One solution isto reduce the number of antennae that emit the same ID, e.g., split alarge number of DAS antennas into two or more time synchronized clusterswith different IDs. Such an arrangement will increase the system cost(increase the number of base stations) and require the handset/UE tosupport the above-mentioned technology.

Employing U-TDOA in a DAS environment will also add cost relative toadding/installing LMU units. However, no changes to the UE (handset)will be needed; only the base station software would have to be upgradedto support the U-TDOA functionality. Also, it is possible to integratemultiple LMUs with (into) a DAS system. Therefore, using the U-TDOAmethod with LMUs has many advantages when utilized indoors, in campusenvironments, and in other GPS/GNSS challenging, geographically limitedenvironments.

Precise time synchronization amongst geographically distributed multiplebase stations and/or small cells and/or LMUs in indoors and otherGPS/GNSS denied environments is more complex than time synchronizingmacro cells and/or the LMU equipment used in the macro cell outdoor,e.g., GPS/GNSS friendly environment. This is because the macro cells inthe outdoor environment have antennas, that are elevated and in theopen. As a result, the GPS/GNSS signal(s) quality is very good and macrocells antennas transmissions and/or LMU receivers can be synchronizedusing GPS/GNSS to a very high accuracy—standard deviation 10 ns, over asufficiently large area.

In an aspect, for indoor and other GPS/GNSS denied environments, timesynchronization amongst multiple distributed base station and/or smallcells/LMUs is achieved by using an External Synchronization Source thatproduces the synchronization signal shared by many base stations and/orsmall cells and/or LMUs. This synchronization signal can be derived fromGPS/GNSS, for example the 1 PPS signal, and/or Internet/Ethernetnetworking, for example PTP or NTP, etc. The latter is a low costsolution, but it cannot provide the time synchronization precisionrequired for accurate location, the GPS/GNSS derived externalsynchronization signal(s) are more precise—standard deviation down to 20ns, but require additional hardware and installation requirements, e.g.wiring up these signals, is more complex/expensive. Also, changes tobase station and/or small cells hardware/low level firmware might beneeded to accommodate the external synchronization signal higher levelof precision. Beside the 20 ns standard deviation is not accurate enoughto satisfy the 3 meters requirements, e.g. standard deviation of about10 ns.

In order to overcome the above mentioned limitations, as illustrated bythe multichannel LMU high level block diagram of FIG. 28, one embodimentuses a LMU device 2800 having multiple receive antennas 2802 and signalchannels 2804. As an example, one or more signal channels 2804 cancomprising signal processing components such as an RFE (RF front end)2806, RF down converter 2808, and/or uplink-locate processor 2810. Othercomponents and configurations can be used. In an aspect, the signalchannels 2804 are co-located within the LMU device 2800 and tightly timesynchronized (e.g., standard deviation of about 3 ns to about 10 ns). Inanother example, antennae 2802 from each LMU signal channel 2804 aregeographically distributed (e.g., similarly to DAS). As a furtherexample, external time synchronization components (e.g., GPS/GNSS,Internet/Ethernet, etc.) can be in communication with the LMU device2800. The Precise time synchronization is more readily achieved insidethe device (e.g., LMU device 2800) than it is by trying to tightlysynchronize a number of geographically distributed devices.

As an example, when two or more multichannel LMUs (e.g., LMU device2800) are deployed, the time synchronization between these LMUs can berelaxed so that a low cost and low complexity approach can be used tosynchronize (using an external source signal) a number of distributedmultichannel LMUs. For example, Internet/Ethernet networkingsynchronization can be used or a common sensor (device) can be deployedto provide timing synchronization between different multichannel LMUs.

On the other hand, the multichannel LMU approach reduces the number ofhyperbolic lines of position (LOPs) that can be used in determining theposition fix, but the time synchronization improvement overcomes thisdeficiency (see explanation and example below).

When using multilateration/trilateration methods, the UE positioningaccuracy is a function of two factors: the geometrical dilution ofprecision (GDOP), which is due to geometrical arrangement of macro celltowers/small cells/LMUs, and the accuracy of single ranging σ_(R_pseudo)measurement (See Gunter Seeber, Satellite Geodesy, 2003):σ_(POS)=GDOP×σ_(R_pseudo)

The GDOP is function of the geographical distribution of transmittingantennas (in case of DL-OTDOA) or receiving antennas (in case ofU-TDOA). In case of the regularly placed antennae, the two dimensionalGDOP estimation is equal to 2/√N (H. B. LEE, ACCURACY LIMITATIONS OFHYPERBOLIC MULTILATERATION SYSTEMS, 1973); where in case of cellularnetworks N is the number of emitters (macro cell towers/small cells/DASantennas) that are “hearable” by the UE (in case of DL-OTDOA) or thenumber of LMUs/LMUs receive channels that can “hear” the UE uplinktransmission (in case of U-TDOA). Therefore, the standard deviation ofUE position error can be calculated as follows:

$\sigma_{POS} = {\frac{2}{\sqrt{N}} \times \sigma_{R\;\_\;{pseudo}}}$

Assume that eight geographically distributed (indoors) single receivechannel LMUs (regularly placed) are detecting the UE uplink transmissionand these LMUs are synchronized via the 1 PPS signal (e.g., standarddeviation of 20 ns). In this case N=8 and there will be sevenindependent LOPs that can be used for UE position fix. Let's furtherassume that ranging error standard deviation, σ_(R) is 3 meters (about10 ns); then the accuracy of single ranging measurement is:σ_(R_pseudo)=√{square root over ((σ_(R) ²)+(σ_(SYNC) ²))}=√{square rootover (10²+20²)}=22.4 ns (6.7 meters);

where σ_(SYNC) is the external time synchronization signal standarddeviation (20 ns).

In this case (N=8) the single ranging measurement and the standarddeviation of UE position error σ_(POS) is equal to 4.74 meters.

As an example, if two, four receive channel LMUs (e.g., multichannel LMUdevice 2800) with regularly placed distributed antennae, are detectingthe UE uplink transmission, then each LMU will produce a set of threetightly time synchronized LOPs (e.g., standard deviation of about 3 ns);and for three independent LOPs the N=4. In this case, two UE positionfixes is generated, each with standard deviation error σ_(POS) of 3.12meters. Combining these two position fixes by averaging and/or othermeans/methods will further reduce the UE position fix error. Oneestimate is that the error reduction is proportional to the square rootof the number of the UE position fixes. In the present disclosure, thisnumber is equal two and the final UE position fix error σ_(POS_FINAL) is2.21 meter; obtained as: 3.12/√2.

In an aspect, several multichannel LMU (e.g., LMU device 2800) withrelaxed synchronization between these multichannel LMUs can be used forindoors and other GPS/GNSS denied environments. As an example, withinthe multichannel LMU device, the LMUs can be tightly synchronized (e.g.,standard deviation of between about 3 ns and about 10 ns). Anotherembodiment takes advantage of the fact that a number of single channelsmall cell/LMU and/or small cells with integrated LMU deviceselectronics (the LMU functionality is embedded into the eNB) can beclustered (e.g., integrated, co-located, etc.) in a rackmount enclosure(FIG. 31, FIG. 32 and FIG. 33) and/or a cabinet, for example a 19-inchrack. Each single channel device antenna can be geographicallydistributed, like in DAS. The devices within a cluster can be tightlytime synchronized (e.g., standard deviation of less than or equal to 10ns). Multiple rackmount enclosures can be synchronized per communicationrequirements, for example VoLTE, whereby a low cost and low complexityapproach can be used. Precise (tight) time synchronization between anumber of devices clustered (integrated) inside the rackmountenclosure/cabinet is more readily achieved and less costly than in thecase of tightly time synchronizing a number of geographicallydistributed devices.

In another aspect, multiple LMUs can be integrated with (into) the DASsystem as illustrated in FIG. 34. As an example, the LMU receivers canshare the received signal(s) generated by the each DAS antenna, e.g.,sharing DAS antennas. The actual distribution of these received signalsdepends upon the DAS implementation: active DAS vs. passive DAS.However, the LMU and DAS integration embodiment entails sharing thereceived signal(s) generated by the each DAS antenna with LMU receiverchannel and creating an almanac that matches (correlates) each DASantenna coordinates with corresponding LMU/LMU receiver channel. Again,the clustering approach and/or employing multichannel LMU(s) arepreferable ways for LMU and DAS integration.

Also, in a similar fashion, it is possible to share the receivedsignal(s) generated by the each small cell antenna with the LMU receiverchannel. Here, the small cell's time synchronization can be relaxed,e.g. does not need to meet the locate requirements, while the LMU/LMUchannels will require precision time synchronization. The clusteringapproach and/or employing multichannel LMU(s) are a preferable way forLMU(s) for such option.

Integration of the LMU and the eNB into a single unit has a costadvantage over a combination of standalone eNB and LMU devices. However,unlike the integrated LMU and the eNB receiver, a standalone LMU receivechannel does not have to process the data payload from UE. Furthermore,because the UE uplink ranging signals (SRS, sounding reference signal,in case of LTE) are repeatable and time synchronized (to the servingcell), each standalone LMU receive channel can support (be timemultiplexed with) two or more antennae, for example serve two or moresmall cells. This, in turn, can lower the number of LMUs (in smallcells/DAS and/or other U-TDOA locate environments) and reduce the costof the system (see also FIG. 28).

If wireless/cellular network E-SMLC server is lacking the functionalityrequired for DL-OTDOA and/or U-TDOA techniques, this functionality canbe carried out by a location server that can communicate with the UEand/or LMU and the wireless/cellular network infrastructure and/or alocation services server (see FIG. 29 and FIG. 30). Other configurationscan be used.

In another aspect, one or more LMU devices (e.g., LMU 2802) can bedeployed with WiFi infrastructure, for example, as illustrated in FIG.35. Alternatively, a listening device could be used to monitor the LMUantenna in the same manner as the WiFi infrastructure. As such, the LMUdevices and/or channel antennas servicing the LMUs can be co-locatedwith one or more WiFi/listening devices 3500, such as one or more WiFiaccess points (APs). As an example, the WiFi devices 3500 can begeographically distributed.

In one embodiment the WiFi device 3500 can be connected to a powersource. An RF analog portion 3502 (e.g., circuitry) of one or more LMUdevices or channels can be integrated with the LMU antenna such that theRF analog portion 3502 can share the power source with the WiFi device3500 (see FIG. 35). As an example, the RF analog portion 3502 of the LMUdevice or channel can be connected via cable to the Uplink-Locateprocessor circuitry (e.g., Uplink-Locate processor 2810), which caninclude the baseband signal processing. As a further example, becausethere can be signal amplification between the antenna and theinterconnecting cable between the RF analog portion 3502 and thebaseband circuitry, such an embodiment facilitates improvedsignal-to-noise ratio (SNR). Moreover, the RF analog portion 3502 candown-convert the received signal (e.g., down to the baseband) and,because the baseband signal frequencies are several magnitudes smallerthan the received signal in antenna, the cable requirements can berelaxed. Such relaxation of cable requirements can translate into costreduction of the connections and can significantly increase thetransmission distance.

It is understood that the ranging signals are not limited to the SRSonly and can utilize other reference signals, including MIMO, CRS(Cell-Specific Reference Signal), etc.

In a further embodiment, the network-centric locating approach of 5G maybe improved by fusing downlink OTDOA and uplink OTDOA methods. Forexample, in the macro-cell environment, the uplink AoA method suffersfrom a lack of range. This is because uplink signal transmission by theUE is orders of magnitude lower in power than corresponding downlinksignal transmissions by the macro-cell. As a result, the probability ofthe UE signals being detected by neighboring cells is low. For uplinkOTDOA, the 3GPP standards contemplate the deployment of LMUs to helpimprove the detectability of the uplink signals (i.e., LMUs areessentially uplink transmission receivers with additional signalprocessing capabilities). The deployment of many LMUs address the uplinksignal detectability issue and enable multilateration positioning forlocating UEs, but there is a significant cost associated with thedeployment and maintaining a large number of such devices. As a result,wireless networks carriers did not deploy LMUs in the LTE 4Genvironment.

Accordingly, in order to enable uplink locating in 4G and 5Genvironments, it may be necessary to leverage serving cell antennasystem characteristics (properties) for different methods of determiningUE position. For example, depending on the serving cell antenna systemdesign, it may be possible to use an uplink method that estimates thedirection (angle) of arrival (DoA/AoA) of the UE reference signaltransmissions in the horizontal (aka azimuth) plane. Theoretically, itmay also be possible to estimate the DoA/AoA in the vertical (akaelevation) plane. By combining DoA/AoA calculations in both thehorizontal and vertical plane, it may be it possible to locate the UE.In practice, however, because of macro-cell antennas designs, DoA/AoAestimates for the vertical plane are too low for precise positiondetermination.

To improve the uplink DoA/AOA method, it may therefore be necessary toestimate a distance from the UE to a serving cell. This distance may bederived from the round-trip time (RTT) and Time Advance (TA)information, which are based on measurements performed by the servingcell. However, because of the radio wave propagation phenomena thesemeasurements may lack required precision. One possible way to addressthe precision issue is to use downlink OTDOA to estimate the distancebetween the UE and the serving cell. The drawback to this solution isthat downlink OTDOA requires an accurate synchronization between theserving cell and neighboring cells that are used for the UE positiondetermination, e.g., the locate accuracy is impacted by thesynchronization error. Uplink OTDOA also suffers from the same effect,so that is no alternative.

However, uplink DoA/AoA is free from this error because only the servingcell is used, i.e. no neighboring cells are used, so no synchronizationis required. In contrast, when downlink OTDOA is used to determine thedistance between the serving cell and the UE, this measurement mayinclude the aforementioned synchronization error, although that errormay be mitigated if downlink OTDOA is only used to determine the initialUE position. Thereafter, as long as the same serving cell is used, theUE position may be calculated by state-of-the art tracking algorithmsbased on UE DoA/AoA and UE velocity (estimated from reference signals asthe DoA/AoA) measurements. Over time (i.e., a number of measurements),the tracking algorithms or tracker will reduce the impact of thesynchronization error. It should be noted that soft/softer handoverbetween serving cells is not supported by 4G LTE, so the initial UEdistance to the serving cell measurement may have to be repeated everytime the UE switched from one serving cell to another serving cell.

Tracking algorithms may be based on radar and sonar performanceenhancement strategies. The tracking algorithms or tracker may providethe ability to predict future positions of multiple moving objects basedon the history of positions of the individual objects reported by sensorsystems. There are a number of different types of trackers, includingparticle filter algorithms, Kalman algorithms, etc. Use of a tracker mayalso improve UE positioning when the initial UE position is calculatedbased on RTT measurements as discussed above. The choice of whether touse downlink OTDOA or uplink RTT/TA to measure the distance from the UEto the serving cell depends on the magnitude of error in the distanceestimation. The larger the error, the more the performance of thetracker may be impacted. In an embodiment, two different independenttrackers are utilized at the same time and use the position of the onethat converges faster (i.e., is first to determine the position). Yetanother embodiment is to utilize a tracker to estimate the distance andto correct the synchronization error. This approach may yield the bestaccuracy.

As noted above, uplink methods are inherently network-centric as therelevant UE reference signal transmissions may be collected andpre-processed by the eNodeB and then forwarded to a LSU for furtherprocessing and UE position determination using one or more networkprotocols. In case of downlink OTDOA, the task of collecting andpre-processing the downlink reference signals is performed by the UE.The UE than sends collected downlink data to the LSU. The UE handles thecommunication with the LSU, using the control plane and/or the LTE userplane. Consequently, signaling may be in line with the OMA Secure UserPlane Location (SUPL) protocol and/or 3GPP, e.g., LTE PositioningProtocol (LPP).

Many uplink and downlink reference signals may be employed for UEposition determination. The most commonly used for uplink locate includethe Sounding Reference Signal (SRS) and/or Demodulation Reference Signal(DMRS). The most commonly used for downlink locate include PositioningReference Signal (PRS) and/or Cell-Specific Reference Signal (CRS). Thereference signals may be collected in a digital format, i.e., samples,which may be pre-processed before being sent to the LSU. The digitalsamples of the reference signal may be extracted from the baseband I/Qsamples in time domain or the baseband I/Q samples in frequency domain,as well as the Resource Elements (RE) from the OFDM symbols. REs arecomplex-valued coefficients representing symbols in frequency domain.The I/Q values represent the signal in-phase (I) and quadrature (Q)components. As a result, the reference signals may be represented by theI/Q samples in time domain or the I/Q samples in frequency domain, orREs of symbols containing the reference signals.

In a downlink OTDOA UE embodiment, both the PRS and the CRS referencesignals may be collected. In other words, this method may utilize PRS,CRS, or both types of signals. This hybrid mode of operation (i.e., PRSor CRS or both PRS and CRS) has the advantage of allowing networkoperator(s) to dynamically choose the mode of operation depending on thecircumstances or certain network parameters. For example, PRS may havebetter hearability than CRS, but using PRS may cause a reduction in datathroughput. CRS do not effect throughput and have a higher referencesignal density, which is advantageous when the UE is moving. Inaddition, CRS is backward compatible with all previous LTE releases, forexample Rel-8 and lower. As such, the hybrid method provides a networkoperator with the ability to trade-off or balance between hearability,throughput, and compatibility.

In an embodiment of downlink OTDOA, the UE receiver may include adetector configured to detect and isolate OFDM symbols that carryingreference signals, for example PRS and/or CRS, from one or more downlinkframes. This detector may be further configured to extract ResourceElements (REs) from the OFDM symbols on a per symbol basis, and may bealso configured to collect and store REs from a number of OFDM symbolson a per symbol basis, i.e., produce for each symbol identification (ID)a downlink data structure that includes REs correlated with this symbolID. In addition, the detector may be configured to collect downlinkmetadata, including each frame start and other relevant and/or auxiliaryinformation.

In an embodiment, an exemplary data structure of the OFDM symbols withCRS RE elements is as follows:

-   -   CRSR_data_sruct: [Number of captured radio slots in one capture        block, Time interval (in number of slots) between capture        blocks, CRS data length and CRS data (the Resource Elements)].

In an embodiment, exemplary metadata transferred from UE to LSUincludes:

-   -   Serving cell information: [physCellId, cellGlobalId,        CellGlobalIdEUTRA-AndUTRA, earfcn-DL, systemFrameNumber, Slot        number, UTC timestamp, rsrp-Result, rsrq-Result,    -   ue-RxTxTimeDiff, DownlinkPathLoss, Bandwidth, in Physical        Resource Blocks (PRB).    -   Neighbour cell information (0 to 32 neighbor cells):        [physCellId, cellGlobalId, CellGlobalIdEUTRA-AndUTRA, earfcn-DL,        systemFrameNumber, rsrp-Result, rsrq-Result.    -   UE information: [UE ID, UE category, Mobility state,        MobilityHistoryReport].

In an embodiment of downlink OTDOA, the UE receiver may include acommunication processor configured to signal and exchange downlink datawith the LSU, including being configured to send RE data and metadata tothe LSU and to receive commands and assistance information. It should benoted that the signaling may be in line with OMA SUPL protocol and/or3GPP LPP, or combination of LPP and SUPL.

In an embodiment of downlink OTDOA, the UE receiver's detector may beconfigured to extract the reference signal I/Q samples in time domainfrom OFDM symbols on a per symbol basis, and also be configured tocollect these I/Q samples in time domain from a number of OFDM symbolson a per symbol basis, i.e., produce for each symbol ID a downlink datastructure that includes I/Q samples in time domain correlated with thissymbol ID. In addition, the detector may be configured to collectdownlink metadata, including each frame start and other relevant and/orauxiliary information.

In an embodiment of downlink OTDOA, the UE receiver's detector may beconfigured to extract the reference signals I/Q samples in time domainfrom OFDM symbols on a per symbol basis, and also may be configured tocollect the I/Q samples in frequency domain from a number of OFDMsymbols on per symbol bases, i.e., produce for each symbol ID a downlinkdata structure that includes I/Q samples in frequency domain correlatedwith this symbol ID. In addition, the detector may be configured tocollect downlink metadata, including each frame start and other relevantand/or auxiliary information.

OFDM symbols carrying reference signals Res may include both payload andreference signals REs. As a result, the REs collected by the detectorfrom a number of OFDM symbols, and consequently the downlink datastructures generated from these collections, include both payload andreference signals REs. When this data is sent to the LSU, the payloadREs becoming an overhead, reducing the uplink capacity.

The location of CRS and PRS reference signal REs in the symbol'sfrequency dimension may be determined by cell ID; antenna configuration;antenna port; the slot number within a radio frame and the OFDM symbolnumber within the slot (see also, 3GPP 36.211 v13 or ETSI TS 136 211V13.0.0). If this information is known to the UE receiver's detector,the detector may be configured to remove the payload REs, therebydecreasing the size of the downlink data transmitted to the LSU, i.e.,reducing the overhead. The amount of reduction may vary depending uponthe reference signal type, neighbor cell IDs and other cell parameters.For example, for CRS, the data size reduction may be 3× (for a cell witha dual antenna sector). In other instances, there may be no appreciablereduction, but this may be rather rare because the reference signal REslocations in the symbol's frequency dimension are repeated on [(cell ID)mod 6] basis. Thus, for the CRS, on average, the data size reduction maybe approximately 40% relatively to a worst case scenario.

In an embodiment of downlink OTDOA, the LSU is configured to process thedownlink reference signal data and other relevant and/or auxiliarydownlink information, including the frame start, that was sent from oneor more UEs. The output of the LSU is the position of the one or moreUEs and other downlink position related metadata, such as ConfidenceRadius Values, FCC NG911 location accuracy metric, etc.

The LSU may include a downlink signal processor configured to estimatethe time of arrival (TOA) and/or time of flight (TOF) of referencesignals from the downlink reference signal data and other informationsent by the UE. This processor is also configured to determine the timedifference (i.e., RSTD/TDOA, where RSTD means Reference Signal TimeDifference) between reference signals from several cells. The downlinksignal processor may include locating signal processing algorithms andother technology and techniques, including multi-path mitigationalgorithms and methods, including advanced spectrum estimationalgorithms, Constant False Alarm Rate (CFAR) detection algorithms,Space-Time Adaptive Processing (STAP), etc. In addition, the downlinksignal processor may be configured to estimate the Doppler shift,utilizing one or more Doppler specific algorithms and/or techniques,which may allow the LSU to track one or more moving UEs and to mitigateUE receiver clock drift. The Doppler shift is used to update (correct)the downlink reference signal data.

The signal processing may be applied individually to reference signalsfrom each antenna for a given cell sector antenna subsystem. The cellsector antenna subsystem may include multiple (two or more) antennas,such as a dual antenna sector antenna subsystem, which are commonlydeployed. These antennae may be spatially separated. As a result, at theUE receiver, the reference signals from each antenna may experience adifferent multipath phenomenon, i.e., each having a different impact onthe strength of the direct line of sight (DLOS) of the reference signalfrom each sector antenna. It should be noted that the DLOS path isfrequently obstructed to various degrees and DLOS signal strength mayoften be significantly (12 dB or more) lower than a reflected signalstrength. On the other hand, even if the DLOS obstruction is severe,because of the RF propagation phenomena there is always a direct pathfor the RF signal to the UE receiver. This direct path is somewhatlonger than the DLOS path, but is much closer to the DLOS path lengththan a reflected path, i.e., has minimal impact on the positiondetermination accuracy.

Because of its nature, the direct path is highly attenuated to beginwith and might be further impacted by the multipath. Subsequently, thedownlink signal processor may compare the results between each antennafrom a given sector because of the possibility that the direct pathsignal might not be detectable for each sector's antenna. It should benoted that the overall signal strength and/or SNR of the receivedreference signal from the sector's antenna does not always correlatewith the DLOS and/or direct path signal strength, and/or SNR.

Furthermore, the downlink signal processor is configured to calculatethe TOA confidence metric, which is for each TOA from each cell and eachcell sector's antenna. This calculation may include whether the overallsignal strength and/or SNR of the received signal meets a desiredthreshold and the TOA signal statistics, such as standard deviation,Mean Absolute Deviation (MAD), etc. This calculation may also includewhether the direct path/DLOS was found and the direct path/DLOS SNR, ifthe direct path was identified. Additional information may includewhether the serving cell is the closest cell and whether the servingcell has the highest SNR, as well as the Geometric Dilution Of Precision(GDOP) calculations for each RSTD combination. It should be noted thatGDOP depends on the geometry of the cell locations. The GDOP may statehow this geometry will affect the final UE position estimate. The GDOPvalues may depend upon the angle at which two given RSTD/TDOA linesintersect. In the best case (where GDOP=1), this angle is at 90 degrees.In the worst case (where GDOP >20), the angle is small. An RSTD/TDOAhyperbolic line may also be called the line of position (LOP).

As in the case of the cell sector antenna subsystem, the UE may includemultiple (two or more) antennas and associate these antennas withmultiple receive channels. Any of these antennas may be receiving areference signal, thereby giving the UE the option of collectingincoming signals from each antenna. The reference signals from eachantenna may be collected and pre-processed by the UE and then sent tothe LSU, as per the aforementioned downlink OTDOA UE embodiments. The UEantennas may be closely spaced which reduces the multipath impactvariation between antennas. However, the UE antennas may be designed forpolarization diversity.

Embodiments of the LSU may include a downlink signal processor thatcompares results between each antenna from a given cell/sector. Thedownlink signal processor may also compare results between each UEantenna. The rationale behind such comparisons is that the antennapolarization phenomenon might result in attenuating some interferenceand/or reflected path while at the same time amplifying the DLOS/directpath signal. In other words, the information redundancy associated withsuch comparisons may increase the probability of the DLOS/direct pathdetection.

In an LSU embodiment, the output (results) from the downlink signalprocessor, including TOA/TOF values, RSTD/TDOA values, a confidencemetric, DLOS/direct path probability, etc., is passed to a navigation(position) processor configured to estimate one or more UE positions andproduce other downlink position related metadata, such as ConfidenceRadius Values, FCC NG911 location accuracy metric, etc.

For downlink OTDOA UE locate embodiments, the LSU may include anavigation processor that utilizes multilateration techniques ormethods, which is also known as hyperbolic navigation. Hyperbolicnavigation is based on the difference in timing, i.e., RSTD/TDOA,without reference to a common clock. The navigation processor may alsobe configured to utilize the information redundancy discussed above,e.g., TOA/TOF, confidence metric, DLOS/direct path probability, etc., tomitigate ambiguities of the multilateration position fix, such as byapplying location consistency algorithms.

Multilateration techniques and methods entail solving a number ofhyperbolic (RSTD/TDOA) equations, for which a number of differentalgorithms/approaches may be used to find the right solution. Somealgorithms/approaches involve iterative methods that may begin with aninitial estimate or “guess” of the location. That estimate may then beimproved with each iteration by determining a local linear least-squareslocation solution. One drawback to this approach is that it requires theinitial location estimate to be pretty close to the final locationsolution in order to guarantee convergence and/or the absence of localminima, which may result in a substantial position error. On the otherhand, it may work well in the over-determined situation where there aremore measurement equations than unknown quantities. In this regard, itshould be noted that an over-determined situation reduces thepossibility of ambiguous and/or extraneous solutions that might occurwhen only the minimum required number of measurements are available.

There are also non-iterative solutions to the hyperbolic positionestimation problem. These solutions are in closed-form and may be validfor both distant and close sources, thereby eliminating the convergenceand/or local minima issues of the iterative approaches. One drawback tonon-iterative solutions is that they require a priori knowledge of theapproximate location to be determined, such as the distance between theUE and a first cell, e.g., the serving cell. Another drawback tonon-iterative solutions is that they are closed-form solutions that arenot designed for over-determined situations. Nevertheless, it may bepossible to make the non-iterative solutions work in the over-determinedsituation by transforming the original set of nonlinear TDOA equationsinto another set of linear equations with an extra variable. A weightedlinear least squares, for example, provides an initial locationsolution, and a second weighted least squares then provides an improvedposition/location estimate using the known constraint of the sourcecoordinates and the extra variable.

In the over-determined situation, it is also possible to have multiplesets of three RSTD/TDOA subsets and to find closed-form solutions foreach subset. Thereafter, location consistency algorithms may be utilizedto finalize the position fix. Moreover, it is also possible to improvethe position fix utilizing a combination of iterative and non-iterativesolutions from the same set of RSTD/TDOA values. In this regard,iterative and non-iterative approaches need to have a close initialestimate of the UE location. This estimate can be enhanced by the TimeAdvance (TA), aka RTT, information. TA is a negative offset at the UE,between the start of a received downlink subframe (frame) and atransmitted uplink subframe (frame). TA is equal to 2× the propagationdelay, or the RTT; TA is available from the serving cell and representsan independent UE range estimate from the serving sector.

Based on the above, the UE may be located along an arc defined by theserving sector's azimuth beam width and the RTT/2 range, aka radius.Because the serving sector's azimuth beam width can be a large value,reaching 120 degrees, the length of the arc quickly grows with thatdistance impacting the initial UE position estimate accuracy. However,the accuracy of the position/location estimate may be improved by virtueof the fact that the UE may also be located by the intersection of theazimuth beam widths of the serving sector and neighboring cell sectors.This approach helps to mitigate arc growth impact. Further improvementmay also be achieved by considering sector antennas mechanical down-tiltangle and/or electrical down-tilt angle, antenna gain, elevation beamwidth (in addition to the azimuth beam width), as well as cell towerheight and tower structure type.

In order to develop an even finer initial UE position estimate, the LSUnavigation processor may be configured to work with a LSU uplink signalprocessor. As with the LSU downlink signal processor, the LSU uplinksignal processor may receive the uplink reference signal(s), for exampleSRS and/or DMRS, data that was collected from one or more UEs andpre-processed by the eNodeB (cell). At the eNodeB, digital samples ofthe uplink reference signals are extracted from the baseband andcollected as uplink reference signal(s) data, together with relevantuplink metadata. The UE uplink data and the relevant uplink metadata isthen sent to the LSU uplink signal processor. The uplink signalprocessor may be configured to determine the AoA observables based onthe UE uplink data and the relevant uplink metadata, as well as theknown configuration of the eNodeB sector antenna array, which isincluded in the uplink metadata. The AoA observables are then sent tothe LSU navigation processor, which may reduce an ambiguity in the AoAobservables and produce the AoA line of bearing (LOB) and/or directionof arrival (DoA) from the AoA observables and the uplink metadata.

The AoA/DoA estimate produced by the LSU navigation processor maydramatically limit the arc growth versus distance from the eNodeB(serving sector). This is because, unlike the sector azimuth beam widththat might reach 120 degrees, the error of AoA line of bearing (LOB)and/or direction of arrival (DoA) estimated by the LSU navigationprocessor is on the order of one degree or less. Thus, compared with thetraditional usage of the sector azimuth beam width, the arc growth whenutilizing the LSU uplink signal processor and LSU navigation processormay be 100× less. Consequently, the initial UE position estimate may be100× more accurate. The AoA/DoA estimate can therefore improve thedownlink OTDOA UE positioning accuracy, and this more accurate UElocation can, in turn, improve the uplink AoA/DoA UE position fix.Accordingly, this enables joint uplink/downlink or downlink/uplink UEpositioning.

Wireless network environments sometimes defy use of the multilaterationmethod, which requires at least three reference points (for 2-D locate)in order to obtain the UE location. For example, in some urbanenvironments, the wireless networks use only two high power cells thatflood a large area with RF signals because for data communicationpurposes, it is not necessary to have a DLOS/direct path to the UE.These communications can be carried using the reflected signals as longas the reflected path delay is less than the Cyclic Prefix (CP) length.This creates a problem for common navigation techniques that rely on atleast three reference points being detectable.

In an embodiment, one solution to this problem (i.e., a two tower/cellenvironment) may be to use one or more heuristic approaches to obtainthe UE location fix, as further illustrated in FIG. 36. The method maystart with plotting the TDOA hyperbolas for the two cell/tower sectors3602 and 3604 that are detectable, such as hyperbolas 3606 and 3608, andfinding the hyperbola to which the target UE belongs. The UE's hyperbolacan be determined by finding which sector azimuth beam from each of thecells/towers 3602 and 3604 that the UE also resides within. Thehyperbola that belongs to both sector azimuth beams is the chosenhyperbola. As illustrated in FIG. 36, cell/tower 3602 has a sectorazimuth beam width 3610, while cell/tower 3604 has a sector azimuth beamwidth 3612. Hyperbola 3608 is in the intersection between sector azimuthbeam width 3610 and 3612, so hyperbola 3608 is the chosen hyperbola. Theintersection 3616 between sector azimuth beam width 3610 and sectorazimuth beam width 3612 is the area of highest probability of where theUE is located.

If TA (RTT) is available, the UE may then be located along an arcdefined by the serving sector azimuth beam width and the RTT/2 range,aka radius. Thus, the UE may be positioned at the hyperbola and arcintersection vicinity. On the other hand, if the AoA/DoA estimate isavailable then the UE belongs to the LOB and the hyperbola. This lattersolution may be more accurate than the former solution.

Yet, when neither TA/RTT, nor the AoA/DoA estimate is available, thenthe UE location can be determined by scoring intersection pointscorresponding to each cell/tower (sector) on the chosen hyperbola 3608.The score may be partly based on a cosine of the difference in anglesbetween the direction a cell/tower is pointing and the direction to apoint on the hyperbola being tested from the cell/tower sector. As shownin FIG. 36, cell/tower 3602 is pointing in the direction 3620 andcell/tower 3604 is pointing in direction 3622, which definesintersection points 3630 and 3634. The remaining part of the score maycome from the distance from each point 3630 and 3634 to thecorresponding cell/tower. Once a score for each point 3630 and 3634 hasbeen determined, the point with the highest score from cell/tower 3602and cell/tower 3604 may be determined. The two best scoring points onthe hyperbola are then weighted according to their correspondingcell/tower's SNR. Finally, a point between the two best scoring points(closer to the higher weighted point by SNR) is picked as the UElocation fix. As shown in FIG. 36, this results in a UE position 3650being estimated, when the true location of the UE is location 3652.

Returning to the discussion of the LSU embodiment, the LSU may furtherinclude a communication processor configured for signaling andinformation exchange with the UE, the eNodeB, and the network elements.The signaling may be in line with the OMA SUPL protocol and/or 3GPPLPP/LPPa, or a combination of LPP, LPPa and SUPL, as well as otherprotocols that are or may be used for communication with the network,for example LCS-AP protocol.

An example of the information so exchanged may be as follows:

-   -   Site Name:        -   Technology (i.e., 4G, 5G), Active (e.g., Y/N)(i.e., On-Air),            In building (e.g., Y/N);        -   Global CellID, PCI value, Frequency, IsGPSsynchronized, DL            Tx configuration (i.e., number of Tx ports, maxTx power, DL            bandwidth);        -   Tower structure type (i.e., rooftop, monopole,            building-side, etc.), cable length and losses; Antenna type            (i.e., omni, directional), Latitude, Longitude, Antenna            altitude AGL (i.e., above ground level), Towerbase altitude            MSL (i.e., mean sea level), Geoid;        -   Antenna Azimuth, Elevation, Mechanical down-tilt angle            Electrical down-tilt angle, Gain, H-Beamwidth, V-Beamwidth;        -   Cell Bandwidth, TA, Neighbour Cells list, PRS configuration;        -   eNodeB Sector antenna array configuration.

In an LSU embodiment, all LSU components/elements (such as thecommunication processor, downlink signal processor, uplink signalprocessor, and navigation processor) may be implemented in software thatmay be executed on one or more network core elements. These LSUcomponents may also be run on evolving 4.5G MEC (Mobile Edge Computing)server at a facility's edge. For example, the LSU components may beintegrated as a hosted app on a 4.5G MEC. Finally, in 5G deployment, theLSU components may be hosted in the core network computing cloud. TheLSU being hosted in the core network computing cloud supports theLocation-as-a-Service (LaaS) data delivery, whereby the UE acts as agateway to the core network computing cloud and LaaS specifically forprotected physical location data.

In accordance with the disclosure above, a network-centric architecturehas been described that supports LaaS data delivery, and which isdesigned for 5G and other networks when all signal processing andposition estimates are done in the cloud, i.e., outside of the EUand/oreNodeB. There are several options in terms of how this can bedone. In the case of uplink locate, the relevant UE reference signalstransmissions can be collected/pre-processed by the eNodeB and forwardedto the Locate Server Unit (LSU) for further processing and UE positiondetermination. In case of the downlink (OTDOA), the task of collectingand pre-processing of the downlink reference signals may be performed bythe UE. The UE may then send collected downlink data to the LSU. In caseof the downlink (OTDOA), the UE may also handle the communication withthe LSU, using the Control Plane and/or the LTE User Plane.Consequently, signaling may be in line with OMA Secure User PlaneLocation (SUPL) protocol and/or 3GPP, for example LTE PositioningProtocol (LPP). In case of the uplink AoA/DoA the eNodeB may handle thecommunication with the LSU, using the LPPa and SLmAP (SLm ApplicationProtocol) protocols.

In accordance with the above, this network-centric architecture mayenable advanced features that were not feasible before. These include:(a) utilizing the downlink OTDOA to determine the distance between theserving cell/tower and the UE and uplink AoA/DoA to determine the UElocation, while also mitigating the OTDOA synchronization error impacton this uplink/downlink UE positioning by utilizing trackingalgorithms/technology; (b) utilizing tracking algorithms/technology toimprove the UE positioning in cases where the uplink AoA/DoA plus RTTmethod is used to determine the UE position; and (c) utilizing trackingalgorithms/technology to estimate and to correct/mitigate thesynchronization error in the downlink OTDOA locating method.

In accordance with the above, downlink (OTDOA) UE locate methods havealso been described where the navigation processor is utilizing themultilateration technique/method, aka hyperbolic navigation. Thismultilateration technique entails solving a number of hyperbolic(RSTD/TDOA) equations. There are iterative methods and non-iterative(close form) solutions. In one embodiment, a hybrid approach isdescribed that splits the number of available (hearable) referencepoints (base stations) to have multiple sets of three RSTD/TDOA subsetsand finds closed-form solutions for each subset. Thereafter, locationconsistency algorithms may be utilized to finalize the position fix. Ina second embodiment, the position fix may be improved utilizing acombination of iterative and non-iterative solutions from the same setof RSTD/TDOA values. In a third embodiment, initial position estimatesmay be determined for the UE location, based on iterative andnon-iterative algorithms, by utilizing the uplink AoA/DoA estimate(s)and the RTT.

In accordance with the above, a wireless network environment has beendescribed that sometimes defies the multilateration method, whichrequires at least three reference points (for 2-D locate) to obtain thelocate fix because only two high power cells are used to flood a largearea with RF signals. Accordingly, a method is described that when RTTis available, the UE is located along an arc defined by the servingsector azimuth beam width and the RTT/2 range. A method is alsodescribed, when AoA/DoA estimates are available, that the UE positionmay be determined at the hyperbola and arc intersection vicinity. Bothof these methods may also be used. When neither RTT nor AoA/DoAestimates are available, the UE location is determined by scoringintersection points corresponding to each cell/tower (sector) on achosen hyperbola 3608. Scoring is based on a difference in anglesbetween the direction a cell is pointing and the direction to a point onthe hyperbola and a distance from each point to the correspondingcell/tower. Scores may be weighted according to their correspondingcell/tower's SNR.

In accordance with the above, the LSU may include a communicationprocessor that is configured for signaling and information exchange withUEs, the eNodeB, and/or network elements. The signaling may be in linewith the OMA SUPL protocol and/or 3GPP LPP/LPPa, or a combination ofLPP, LPPa and SUPL, as well as other protocols that are or may be usedfor communication with the network, for example LCS-AP protocol.

In accordance with the above, the LSU components may be instructionsstored in memory and configured to execute on a processor of a 4.5G MEC(Mobile Edge Computing) server located at a communication network'sedge, The LSU components may be integrated as a hosted app on 4.5G MEC.In a 5G deployment, the LSU components may be hosted in the core networkcomputing cloud. The LSU being hosted in the core network computingcloud may support LaaS data delivery, whereby the UEs act as gateways tothe core network computing cloud and LaaS specifically for protectedphysical location data.

In accordance with the above, the LSU may include a downlink signalprocessor as well as an uplink signal processor and a navigationprocessor.

For the purpose of device (UE) localization the only requirement is thatthe signal used for this purpose be known in advance. Similarly, suchsignals can be also used for interference/noise cancellation. While theaforesaid requirement typically excludes payload (data) signals, itincludes a large number of other LTE 5G and/or LTE 4G signals, forexample synchronization, reference, pilot, sounding signals, etc.

On the other hand, advance knowledge of the signal may not be needed forthe purpose of localization and/or interference/noise cancellation ifthe ideal states of a digitally modulated received signal are known inadvance and/or can be estimated, e.g., states that were used in thetransmit process of said signal. In this case, the aforementioneddigitally modulated received signal can be used for the purpose oflocalization and/or interference/noise cancellation, including payload,supervisory and other signals.

In wireless networks the reference/pilot, control and some other signals(hereafter referred to as “the reference signals”) may be frequencyshifted relatively to the one used by the serving cell, and relativelyto each other, reducing the interference to these reference signals thatare received from the adjacent cells' signals. Through proper networkengineering, the aforementioned signals that will utilize samefrequencies as the serving cell will usually come from further awaycells.

Thus, the distance from the UE to these interfering cells is much largerthan the distance from the UE to the serving cell, resulting insignificant signal attenuation relative to the serving cell's signals.Furthermore, in many instances the reference signals are generated withsequences which have low cross correlation properties, leading toreduction of the interference between any two signals (sequences).Consequently, the neighboring cells' signal impact is well mitigatedwhen the UE is communicating with the serving cell as is typical fornormal cellular communications. For example, in LTE cellular wirelessnetworks there are 6 subsets of the available OFDM sub-carriers for agiven cell that are determined using modulo six from the Cell ID (PCID).This means that any interference of the aforementioned signals generatedby neighboring cells that utilize same frequencies as the serving cellwill usually come from farther away distances.

As was mentioned before, these “reference signals” may be known inadvance and can be used to derive observables for device (UE)localization algorithms. Many localization techniques/algorithms requireobservables from multiple cells; and the localization accuracy increaseswith the number of observables obtained from geographically distributedcells, e.g., the number of individual cells. Thus, it is alwaysdesirable to have a reliable signal detection, which depends on acertain signal to noise plus interference ratio (SNIR) value, from alargest number of neighboring cells, including cells with same frequencyshift (sub-carrier subset) as the serving cell.

Unlike the case where a device is communicating with the serving cell,in the device localization case it is the serving cell's strong signalsthat interfere with the weaker signals of interest from one or moreneighboring cells. The worst-case scenario is when the serving cell anda neighboring cell signals have the same frequency shift (sub-carriersubset). While the low cross correlation between these signals improvesthe desired signal power to interferer ratio, the interferer (signalfrom the serving cell) is much stronger thereby substantially loweringthe desired signal to interferer margin (ratio) provided by the signals'low cross correlation a.k.a. orthogonality property.

Also, cellular and other wireless networks experiencing the near-fara.k.a. hearability phenomenon, when a UE receiver captures a strongsignal from the serving cell and thereby makes it impossible for thereceiver to detect a weaker signal from one or more neighboring/adjacentcells.

Because “the reference signals” are known in advance a correlationoperation is performed across all of “the reference signals” carryingspectrum (sub-carrier subset). This correlation operation generating(derive) correlation coefficients between “the reference signals”received from the serving cell and known in advance, a.k.a., idealreference signals. Thereafter, these correlation coefficients are usedto cancel the serving cell signal in symbols that include “the referencesignals” from the serving and one or more neighboring cells.

Accordingly, in the present invention embodiment, the detector and/orserver is configured to cancel signals from the serving cell from thereceived signal. Thereafter, the residual signal left after the servingcell signal cancellation process is used for improved detection of “thereference signals” from a neighboring cell signals that have samefrequency shift (sub-carrier subset) as the serving cell; also reducingthe issues incident to the near-far phenomenon.

At the same time, this cancellation's process improved detection benefitis only for signals that share the same frequency shift (sub-carriersubset) as the serving cell. This is simply because a set of usablecoefficients to perform cancellation are the ones derived from theserving cell's “the reference signals” content.

Therefore, in another embodiment said cancellation process is modifiedto enable the cancellation of “the reference signals” sequences that aretransmitted in the symbol locations associated with other antennaport(s) from the serving cell as well as cancelling “the referencesignals” for cells that do not share frequency shift value (sub-carriersubset) with the serving cell. However, in the latter case thecancellation process with “the reference signals” may be impacted by thedata transmitted by the serving cell in the frequency spectrum(sub-carrier subset) occupied by said “reference signals” sequences.

This modification is taking advantage of wireless networks' certainsymbols that include “the reference signals” and have a modulationscheme, for example QPSK, that is known in advance, whilst thismodulation scheme remains unchanged for the whole spectrum of a givensymbol. Performing channel equalization based on “the reference signals”that are known in advance allows to determine the channel model.Afterwards, the equalization of the rest (non “reference signals”) ofsymbol's spectrum is carried out by various techniques, for examplethrough interpolation. Applying the knowledge of the modulation schemeto the equalized non “reference signals” portion of spectrum is makingpossible to estimate the most likely digitally modulated states thatwere used in the transmit process of the non “reference signals”, i.e.,the other signals in the symbol in addition to one or more of “thereference signals”. Thus, making it possible to reconstruct/estimate theideal states, also known as constellation points, of the digitallymodulated received symbol signal over its entire spectrum.

Having obtained these constellation points, the next step is to derivecorrelation coefficients (via correlation with reconstructed idealstates) for all (the entire) modulated serving cell's received symbol'ssignal spectrum. With these correlation coefficients, it then possibleto cancel much more of the serving cell signal in aforementionedsymbols.

This additional modification will lead to further improvement indetection of signals from neighboring cells; detecting a larger numberof neighboring cells and reducing the device localization error.Additionally, the serving cell signal cancellation will also beenhanced, thus enabling the serving cell signal cancellation for cellsthat do not share frequency shift value (sub-carrier subset) with theserving cell.

On the other hand, the number of symbols that meet the abovementionedconditions is typically lower than the overall number of symbols thatthat include “the reference signals”. Hence, limiting the potential ofpossible detection improvement of signals from neighboring cells.

Examples of symbols that meet said conditions include the LTE 4G CRScarrying symbols (see Example A below). In particular, the very firstsymbol of each subframe, which carries CRS and has only the QPSKmodulation scheme (constellation) for all symbol's signals (spectrum).In other words, all of the symbol's sub-carriers are only QPSK modulatedbecause these first subframe symbols are carrying some form of controlor supervisory content, which has to be extremely robust. At the sametime, each LTE 4G subframe includes a total of four symbols carrying CRSsequences, but only one such symbol has the QPSK modulation scheme thatis known in advance and remains unchanged for the whole symbol'sspectrum (sub-carriers). Accordingly, there will only be ten usable CRSsymbols in a frame (vs. a total of forty) for which thistechnique/modification will work.

Example A

In an embodiment, the exemplary extended cancellation process is appliedto the very first CRS (Cell Specific Reference Signal) symbol of eachsubframe, which carries only QPSK constellation in all the sub-carriers.The aforesaid modulation information (knowledge in advance) allows forderiving the ideal states, constellation points, from the receivedsignal. The process is as follows:

Step 1: The serving cell's signals equalization is performed. Theequalization is based on all received CRS symbols, including the firstCRS symbol of each subframe. Note: typically, the equalization of thesub-carriers between ones that carry the CRS, is derived throughinterpolation. Thus, an equalization can be determined for the entiresymbol, for example all 600 subcarriers for Category 1, 72 subcarriersCat-M, etc.

Step 2: The equalizer is applied to the first symbols of each sub-frameto generate the most likely (reference) constellation points that wereused in the transmit process.

Step 3: The reference constellation points are utilized to performcorrelation across all the modulated subcarriers of the serving cell'sentire receive signal at the first symbol locations (of each subframe);thereby generating correlation coefficients.

Step 4: The correlation coefficients are used to cancel all of theserving cell signal sub-carriers in these first symbols in eachsubframe, resulting in serving cell signal cancellation in neighboringcells with other than the serving cell frequency shift values.

In order to alleviate the above described limitations on useablesymbols, it is necessary to expand the ability to estimate the digitallymodulated signal states (constellation points) for signals for whichthere is no advance knowledge of the modulation schemes (formats) usedby the transmit process. Furthermore, some types of modulations, such asthe various quadrature amplitude modulation (QAM) formats, mightgenerate symbols' states (constellations) with forward error correction(FEC) information embedded in them. As a result, it is also necessary totrack the FEC states.

At the same time, there is useful data generated by the receiver duringthe received signal equalization and demodulation operations. Listedbelow are extended cancellation process exemplary embodiments forobtaining such data:

1. According to an embodiment, the aforesaid information can be obtainedfrom the receiver (from the received signal equalization anddemodulation).

a. The most economical approach as it reuses available resources, i.e.the receiver.

b. However, in this embodiment only the serving cell signal is processedby the receiver limiting the ability to cancel interference from cellsthat are other than the serving cell. It is not uncommon for signalsfrom one or more of the neighboring cells to be on a par or even exceedthe serving cell's signal strength, wherein making them potentinterferers.

2. In another embodiment, the received signals digital samples (infrequency and/or time domain) can be sent to the remote server forequalization/demodulation processing.

a. This embodiment allows to process signals from the serving cell aswell as neighboring cells in a serial, i.e. one at the time, or parallel(two or more at the time) fashion. This ability enabling interferencecancellation from both: the serving cell and one or more neighboringcells.

b. The drawback is that sending aforementioned digital samples producesan overhead for the uplink data network's throughput.

3. Yet, another embodiment, performing said equalization/demodulation bya logical entity, a.k.a. detector, residing in the UE/network device.

a. Here, the logical entity can process signals from serving cell and/orone or more neighboring cells.

b. Signals from individual cells can be processed either one signal atthe time by the logical entity (detector) and/or server.

c. By a combination, whereby the serving cell signal is processed by theUE receiver while the logical entity and/or server is simultaneouslyprocessing one or more neighboring cells' signals.

d. This embodiment is reducing overhead for the uplink data throughputbut demands additional computational resources.

Embodiments described above will produce the desired information fromthe receive signals in the form of ideal states (constellation point)estimates. These estimates are the closest approximation of the idealsymbols signals' constellations (states) at the transmitter.

When the received signal states/constellation points are part of a FECprocess, the decision about which signal constellation point/state wastransmitted, may be delayed as compared to other states/constellationpoints. Yet, this phenomenon does not impact the localizationfunctionality/performance because the delay is limited to severalsymbols in time, for example less than 0.01 second.

Another phenomenon of the FEC process is a possibility of decisionambiguity during states/constellation points estimation. In presentembodiments, this ambiguity can be mitigated by zeroing out these(ambiguous) states/points. Furthermore, the zeroing out technique may beextended to one or more symbols' signals of high ambiguity, e.g.,generating multiple ambiguous states/constellation points. The extendedcancellation process may be further enhanced. Below are possibleembodiments for this enhancement:

1. A correlation process is performed on every (successive) individualneighboring cells, i.e., cells with different IDs, and the correlationresponse “quality” assessment may be made.

2. Responses that are deemed to be of relatively high “quality” may thenbe processed to allow cancellation of their cells signals' content fromthe received signal.

3. The resulting received signal residual may then be presented to thenext step of the correlation process involving the next cell site'ssignals of interest.

Because, each neighboring cell has its correlation response assessed for“quality”, this technique works without a need for reprocessing of theearliest of the correlated outputs.

Yet, in another embodiment an iterative technique is employed, wherebythe correlation is repeated for previously processed cells signals todetermine if the correlation “quality” has improved after later cellsignals are removed from the original received signal.

In embodiments, received signals processing is applied to allsymbols/signals that are received during the position occurrence datacollection, e.g. data collection for a position fix.

Listed below are benefits of the embodiment of the extended and enhancedinterference cancellation process:

1. Utilizing all of the received signal spectrum for localization,improving the signals SNIR/detection ability which enables betterlocalization accuracy.

a. Typically, “the reference signals” are using only a portion of thetotal available symbol's spectrum. For example, only every sixth orthird sub-carrier of the LTE 4G symbol's spectrum is employed by theCRS.

2. The ability to utilize all of the received symbols for localization,i.e., not limited to symbols' carrying “the reference signals” and/orsome other symbols signals, including these for which the modulationscheme is known in advance.

a. Improving SNIR and detection ability.

3. Mitigates the correlation output time domain aliasing ambiguity,resulting from “the reference signals” utilizing only a portion of thesymbol's spectrum.

a. For example, in case of CRS only every third of symbol's sub-carrieris occupied by the CRS, e.g. a portion of symbol's spectrum.

4. Mitigate carrier frequency offset (CFO) estimation ambiguity producedwhen two or more frames are integrated because of the repetition of the“the reference signals”, for example same CRS, sequences repeated ineach LTE 4G frame.

As previously noted, for localization accuracy improvement, it is alwaysdesirable to obtain observables from the largest number of neighboringcells. However, the time delays of signals from far remote (relativelyto UE) cells can exceed the Cyclic Prefix (CP) duration. In cases wherenot all available signals are utilized, the symbols used for determiningobservables are not contiguous in time, thereby impacting the accuracyof timing estimation of these delayed signals and producing interferencefrom preceding symbols' signals.

However, in the aforementioned embodiments received signal processingincludes all symbols/signals that are received during the positionoccurrence data collection for the purpose of determining observables.Accordingly, the embodiments accessing and processing the time domaindata including the cyclic prefix for all of the available symbols,thereby cancelling early cells signals, and then computing the correcttiming for delayed signals from remote cells while adjusting thecorrelation output for the computed delays. Thus, circumventing(mitigating) the said phenomenon.

The LTE 5G brings new and powerful capabilities to support high-speeddata rates, improved connectivity and system capacity. In addition tousing sub-6 GHz frequencies, 5G is also being deployed in the millimeterwave spectrum (28 GHz up to 100 GHz frequencies), which hassignificantly greater bandwidths. There are also changes in the physicallayer including the frame structure, reference signals, etc., as well asin the scheduling and transmission modes.

At the same time, the methods/techniques and algorithms, includingsignal processing and locate observables post-processing and/orlocalization, described herein and in related applications may beapplied to the 5G environment (physical layer and infrastructure).

At sub-6 GHz frequencies, in addition to the LTE 4G base station sectorantenna subsystems, the 5G is contemplating deployment of massive MIMOantenna subsystems to simultaneously direct separate data streamstowards multiple users, a.k.a. spatial multiplexing beamforming. Inother words, a massive MIMO antenna subsystem is capable ofsimultaneously serving a plurality of UEs in the same time-frequencyresource. The LTE 4G base station sectors also employ MIMO, which is inthe form of multiple sector's antenna arrays. Antenna elements in eacharray are not controllable/not accessible. As a result, each antennaarray has a fixed azimuth and elevation beam widths and beam directions.5G systems take the LTE 4G MIMO concept further to a massive MIMO byemploying a very large number of controllable/accessible antennaelements at the base station. Thus, massive MIMO is the extension oftraditional LTE 4G MIMO technology to antenna arrays having a largenumber of controllable antenna elements.

The essence of the massive MIMO theory is as follows: as the number ofelements approaches infinity, only the signal content, aimed at a givenUE, survives while interference and noise content tends to zero out. Atthe same time, it turns out that this analysis holds in practicalsystems if the number of base station antennas is much greater than thenumber of served mobiles. For example, a 5G sub-6 GHz massive MIMOconsists of 8×8 (total of 64) antenna elements.

Beamforming or spatial filtering is a signal processing technique thatuses an antenna array for directional signal transmission or reception.Usually, beamforming (specifically, adaptive beamforming) is defined asthe ability to steer a lobe of power in a particular direction toward auser, while the spatial environment that the antenna array and user arein is not generally considered. The advantage of such adaptivebeamforming is a high transmit/receive antenna gain in the desiredspatial direction, while suppressing of the signal to/from undesireddirections. In LTE 5G, the millimeter wave spectrum adaptive beamformingtechniques are used to overcome the high path loss experienced at thesehigher frequencies.

On the other hand, for the massive MIMO (or just multiuser MIMO, a.k.a.,MU-MIMO) in order to perform the spatial multiplexing beamforming, thespatial channel between antenna elements and user terminals needs to becharacterized. Specifically, the aforementioned spatial channelinformation applied to signals transmitted from the antenna arrayproducing data streams to different UEs. Such filtering can also be usedto linearly combine the data received by the antenna array, so that thedata streams from different users can be discriminated.

Adaptive beamforming requires a large number of antenna elements toproduce a high gain in a desired spatial direction with a steered beam.This condition limits the number of aforementioned beams that can begenerated from a given number of antennas elements. On the other hand,spatial multiplexing beamforming uses all antenna elements to produce alarger number of spatially multiplexed beams at a lower spatial gain.

At sub-6 GHz frequencies the path loss is not a dominant factor.Consequently, there is no need for a high transmit/receive antenna gain.In this case, for a finite number of controllable antenna elements, thenumber of simultaneous spatially multiplexed data streams is limited bythe interference. The estimated number of simultaneous data streams fora 5G sixty-four elements (8×8) MIMO is ten, sometimes larger, thusproviding streams to approximately ten users. A further increase innumber of streams, will require a massive MIMO with a larger (>64)number of elements.

However, in the LTE 5G millimeter wave spectrum the path loss is thedominant. As a result, the massive MIMO is used to produce high gainsteered beams; and despite the larger number of the massive MIMO'santenna elements, for example 256 or more, the estimated number of beamsis about four (providing beams to approximately four users).

For a UE there are two possible antenna subsystem implementationoptions:

1. Each UE has one or more antennas; and

2. The UE is equipped with an antenna array (where all antenna elementsare controllable and fully accessible).

The first option enables communications with Internet of Things (IoT) UEdevices, which in most cases are limited to a single antenna. If usingthe second option, the primary purpose of the UE's antenna array mightbe to form an adaptive beam when operating in the millimeter wavespectrum. In the case of the first option, these extra antennas are mostlikely be used to mitigate interference when utilizing spatiallymultiplexing while operating at sub-6 GHz frequencies.

There are many possible antenna element configurations and deploymentscenarios for a massive MIMO base station, including rectangular,cylindrical, linear, distributed, etc.

The related applications describe the uplink AOA estimation processusing the estimated time of flight (TOF) and/or time of arrival (AOA)difference, e.g. TDOA, of the wave to each of the LTE 4G base stationsector's antenna array (see above). This process generates angulationlocation observables.

On the other hand, unlike the LTE 4G, all of the 5G base station'smassive MIMO antenna elements are controllable and fully accessible.Thus, in embodiments, the uplink AOA estimation process that isdescribed herein or in related applications is applied to two or morecombinations of the massive MIMO antenna elements producing one or moreazimuth and elevation AOA(s) and/or horizontal and vertical DOA(s)observables or LOB(s) from a single tower and/or multiple towers. Inaddition, the massive MIMO antenna elements accessibility feature makesit possible to produce a plurality of TDOA(s) and/or AOA/DOA/LOBestimates for the same UE that, upon further processing, will result inbetter UE position estimation accuracy and robustness by the way ofemploying a data fusion process.

In LTE 4G each base station sector's antenna array is sending orthogonaldownlink pilot/reference signals, for example CRS and/or PRS.

In embodiments, in the LTE 4G environment, with a single antenna UEreceiver detecting this antenna array specific downlink referencesignals, the aforementioned AOA estimation process (described in relatedapplications) is applied. This process utilizes the flight timedifference (TDOA) of the wave from each of the LTE 4G base stationsector's antenna array in order to determine the AOA (a.k.a. angularobservable), which is relative to the base station's sector's antennaarrays baseline.

In another embodiment, when the UE has multiple antennas, these extraantennas may be used to improve the UE localization accuracy androbustness. Here, exemplary embodiments include combining signals frommultiple antennas for better SNR (Signal to Noise Ratio) and/or SINR(Signal to Interference plus Noise Ratio); fusing angular locationobservables from each antenna; selecting location observables from oneor more antenna(s) based on shortest TOF(s) and other criteria, forexample SNR/SNIR, antenna polarization, etc.; or utilizing thisredundant AOA information in the location consistency algorithms.

In yet another embodiment, in the LTE 4G environment the AOA may bedetermined by the UE with two or more antennas and associated with thesereceivers by employing the AOA estimation process described in therelated applications. This process would be able to obtain the AOAestimate for each base station's sector's antenna array relative to theUE antennas baseline. Further processing can be applied to two or moreAOA estimates from each of the base station sector's antenna arrays toimprove accuracy and reliability, for example providing this UE AOAinformation to the location consistency algorithms. It should be notedthat this technique of the AOA estimation will require UE's antennasbaseline direction to be monitored (measured) or known in advance.

Furthermore, the TOF/TOA difference (TDOA) based AOA estimation process(described in the related applications) is mitigating the impact oflimited number of antennas on the number of multipath components thatone can resolve.

The aforesaid LTE 4G embodiments can be utilized in other wirelessnetworks, for example Wi-Fi. Here, the AP (Access Point) iscorresponding to the base station and a Wi-Fi enabled device iscorresponding to the UE.

Compared with LTE 4G, the 5G base station's massive MIMO may senddownlink pilots/reference signal from each of the antenna's element inan orthogonal manner, consuming the significant amount of radioresources such as time and bandwidth.

Currently 5G is assuming the TDD (Time Division Duplex) mode ofoperation because in the TDD mode uplink (UL) and downlink (DL)transmissions use the same frequency resource, permitting the UL/DLchannel reciprocity assumption.

The reciprocity assumption means that the RF (propagation) channel onlyneeds to be characterized in one direction. Therefore, in the TDDsystems the state of downlink channels can be inferred from that ofuplink channels using channel reciprocity. Thus, the base station canestimate the downlink channels without any downlink pilots/referencesignals. Notes: 1) The uplink works in the same way in the TDD and FDDmodes, while the distinct difference of TDD appears in the downlink, and2) In LTE 5G uplink pilot/reference signals do include the SRS (SoundingReference Signal) and the DMRS (Demodulation Reference Signal) signals,employed in the LTE 4G.

The TDD system does provide a remedy for the pilot/reference signalsoverhead issue, but many current wireless systems are operating in thefrequency division duplex (FDD) mode. Thus, it is important to employ(operate) the massive MIMO for the existing FDD systems/mode.

In the absence of orthogonal DL pilots/reference signals from each ofthe massive MIMO's antenna elements, it is not possible to enable theaforementioned AOA estimation process using downlink signals.Nonetheless, it is possible to determine the UE position using the 5GLTE DL signals by employing the multilateration position estimationprocess described in related applications utilizing the LTE 5Gdownlink's one or more reference signals' and/or known in advancesignals' subcarriers. The process is determining these subcarriers'phase and amplitude that are in turn applied to the multi-path processorfor multipath interference mitigation and generation of range (time)based location observables, for example TDOA(s), TOF(s), TOA(s), etc.

In embodiments the multilateration position estimation for the LTE 5Guplink is identical to the process for LTE 4G, as described in therelated applications. This is because the LTE 4G and LTE 5G both supportthe same SRS and DMRS uplink reference signals.

In LTE 4G, the DL multilateration position estimation process utilizesdownlink reference signals, including CRS and/or PRS signals. Althoughthe LTE 5G may not support the LTE 4G downlink CRS and PRS signals,other LTE 5G downlink known in advance signals can be used for thispurpose, including PSS (Primary Synchronization Signal), SSS (SecondarySynchronization Signal); LTE 5G specific ESS (Extended SynchronizationSignal) and Phase Noise Compensation Reference Signal (DL PNCRS), CSI-RS(Channel-State Information Reference Signal), etc.

PSS, SSS and ESS signals are transmitted, for example in symbols 0 to 13in subframes 0 and 25; occupying total of 18 consecutive resource blocks(RB) at the baseband's center.

In embodiments, these PSS, SSS and ESS signals' structure and contiguousresource(s) allocation can be advantageously used for furtherlocalization accuracy and robustness improvement. Exemplary embodimentsmay include combining PSS, SSS and ESS signals' subcarriers phase andamplitude that are in turn applied to the above-mentioned multi-pathprocessor for multipath interference mitigation and generation oflocation observables and/or employing a data fusion process that isutilizing each of PSS, SSS and ESS signals' observables.

The LTE 5G downlink UE positioning may be also based on the LTE 5Gspecific, i.e., new, reference signals supporting beamforming and/orspatial data streams multiplexing and include the downlink BRS (BeamReference Signal) signal. The present embodiments may utilize these newLTE 5G downlink signals and technology to determine the UE position.

For a UE to send and receive data, it must first connect to the networkby acquiring a beam's spatial stream. The SGNB (5G Node B) periodicallytransmits beams at different angles by transmitting BRS (Beam ReferenceSignal). This is known as beam sweeping. When the UE identifies thestrongest beam, it starts a Random Access Procedure, using timing and/orangular information, to connect to this beam. When the UE has connectedto a beam, data transfer can take place on the UE-specific (dedicated)beam. Here, the BRS signals for different beams and/or base stations areorthogonal.

During the sweep multiple beamformed downlink BRS signals transmitted atdifferent angles. The number of BRS beams transmitted and theperiodicity of beam sweeping is determined by the BRS configuration andtransmission period parameters. The BRS occupies eight subcarriers (5ththrough 12th subcarrier) in every RB except the eighteen RB(s) at thebaseband's center, which are allocated for PSS, SSS and ESS signals. TheBRS reference signal is in the form of a Gold sequence (Pseudo RandomData). As a result, multiple beams from one or more base stationscarrying orthogonal BRS signals can be generated.

The UE maintains a candidate beam set of a number of beams, for examplefour. For each beam the UE reports the Beam State Information (BSI). BSIis based on measurements of BRS, and the reported parameters are BeamIndex (BI) and Beam Reference Signal Received Power (BRSRP). The beamsare organized based on the best carrier-to-interference and noise ratio(CINR) measured for the (each) BRS (beam), rather than BRSRP value.

From the above, the beam selection is based on the best CINR. While thisselection criterion is optimizing data communications performance, foraccurate localization it is necessary to identify the beam that is alongthe direct path of signal propagation or direct line of sight (DLOS)between the UE (antenna(s)) and the base station antenna subsystem. Thebeam that is closest to the direct path or DLOS might not have the bestCINR because in many instances the direct path or DLOS may beobstructed, significantly reducing this beam signals' strength. Also,because of RF propagation phenomena, for example multi-path, this directpath or DLOS signal may be further attenuated. As a result, it is notuncommon for a UE to select (lock onto) a beam that undergoes one ormore reflections and has a considerably higher strength and CINR thanthe direct path or DLOS beam. However, generating observables(angulation and/or time/range) from a reflected beam will degrade thepositioning accuracy.

In present embodiment, for each (detectable by UE) beamformed BRSsignal, the signals time of flight (TOF) or time of arrival (TOA) isestimated via the process described in the related applications. Thisprocess is determining those beam signals subcarriers phase andamplitude that are in turn applied to the multi-path processor formultipath interference mitigation and generation of sub-sample precisiontime/range location observables (from BRS signals), for example TOF(s),TOA(s), TDOA(s), etc. Thereafter, the lowest (shortest) TOF criterion isapplied to identify one or more candidate beam(s) that is (are) closestto the direct path or DLOS.

The present embodiment is locating UE based on known (provided by thenetwork) localization candidate beams angular information, azimuth,elevation, beam-widths, etc., on per beam bases; known base station'santenna subsystem height and observables timing information, like TOF,TOA, TDOA, etc., of each localization candidate beam that is estimatedusing the aforementioned process; as well as the RTT (Round Time Trip)measurements (from base station) and/or the receive-transmit timingdifference (RTTD) from the UE.

In present localization embodiment, the UE position in 2D can beestimated, for a given UE altitude, based on the localization candidatebeams from a single base station. The 3D positioning, i.e. determiningthe UE's 2D position plus the UE's altitude, will require localizationcandidate beams from two or more base stations.

In the localization embodiment, the RTT and RTTD information isredundant. However, in another embodiment this information is used bythe location consistency algorithms for position error reduction andreliability (robustness) improvement. For example, the estimated UElocation should fall within the RTT/RTTD distance range that is based onthe measured (reported) RTT/RTTD values and these values standarddeviation.

Yet in anther embodiment, when two or more base stations are used todetermine the UE position, the position estimate should be in thevicinity of one or more LOB(s) (Line of Bearing) defined by one or moreTDOA(s) calculated from TOF/TOA observables estimated for each basestation utilizing the above-mentioned process that is described inrelated applications.

During the beam sweeping, each of the aforementioned beams may include,in addition to the BRS, one or more beamformed DL reference, pilotand/or known in advance signals. The embodiments advantageously usethese signals for further localization accuracy and robustnessimprovement. Exemplary embodiments generating location observables fromany one or more individual PSS, SSS and ESS signals; or a combination oftwo or more such signals subcarriers using the aforementioned multi-pathprocessor described in related applications. The exemplary embodimentsutilize a data fusion process of observables from DL reference, pilotand/or known in advance signals and from BRS signals observables. Otherexamples include selecting DL reference, pilot and/or known in advancesignals' observables based on shortest TOF(s) and other criteria, forexample SNR/SNIR, etc.; or utilizing these observables in the locationconsistency algorithms.

When a UE has two or more standalone antennas the embodiments exploitthese multiple antennas for position error reduction and reliability(robustness) improvement. Exemplary embodiments may include combiningsignals from multiple antennas for SNR (Signal to Noise Ratio) and/orSINR (Signal to Interference plus Noise Ratio) enhancement; fusinglocation observables from each antenna; selecting location observablesfrom one or more antenna(s) based on shortest TOF(s) and other criteria,for example SNR/SNIR, antenna polarization, etc.; calculating the AOAutilizing the aforesaid AOA estimation process that is described in therelated applications and providing this UE AOA information to thelocation consistency algorithms (the AOA determination will require theUE's antennas baseline direction to be monitored or known in advance).

When the UE antenna subsystem is an antenna array the embodiments alsoexploit the UE antenna array for position error reduction andreliability (robustness) improvement. With all antenna array elementsbeing controllable/accessible, exemplary embodiments may include the AOAestimations (azimuth and elevation) utilizing one or moresuper-resolution (sub-space) algorithms (methods) as described in therelated applications; employing the aforementioned TDOA based AOAestimation process that is described in the related applications(applied to two or more combinations of antenna array elements),producing one or more azimuth and/or elevation AOA(s) estimates.Thereafter, this AOA information will be used the location consistencyalgorithms (the AOA determination will require UE's antennas baselinedirection to be monitored or known in advance).

It should be noted that in the 5G environment reference/pilot signalsmay perform multiple functions. For example, the ESS (ExtendedSynchronization Signal) functionality can be carried out by the PTRS(Phase Tracking Reference signal); the downlink CSI-RS (Channel StateInformation Reference Signal) can be used for beam management and inlieu of the BRS (Beam Reference Signal) functionality, etc.

Having thus described the different embodiments of a system and methods,it should be apparent to those skilled in the art that certainadvantages of the described method and apparatus have been achieved. Inparticular, it should be appreciated by those skilled in the art that asystem for tracking and locating objects can be assembled using FGPA orASIC and standard signal processing software/hardware combination at avery small incremental cost. Such a system is useful in a variety ofapplications, e.g. locating people in indoor or in outdoor environments,harsh and hostile environments etc.

It should also be appreciated that various modifications, adaptations,and alternative embodiments thereof may be made within the scope andspirit of the present invention.

What is claimed:
 1. A method for determining a location of a wirelessmobile device in a wireless system, the method comprising: at adetector: buffering a plurality of samples in a digital format generatedfrom signals provided by one or more receive channels; receivingreference signal parameters that describe known in advance referencesignals within the signals; detecting the known in advance referencesignals from each receive channel among the receive channels based onthe reference signal parameters; isolating symbols carrying each knownin advance reference signals from one or more frames; extractingresource elements or I/Q samples from the symbols on a per symbol basis;and sending the extracted resource elements or I/Q samples to a locateserver physically remote from the detector; at the locate server:receiving the extracted resource elements or I/Q samples from thedetector; estimating locate observables for the wireless mobile devicebased on the extracted resource elements or I/Q samples; and calculatingthe location of the wireless mobile device based on the estimated locateobservables, the reference signal parameters and extracted resourceelements or I/Q samples.
 2. The method of claim 1, further comprisingone or more of: determining at the locate server wireless mobile deviceposition related metadata, including Confidence Radius Values and alocation accuracy metric; wherein buffering at the detector includesbuffering the plurality of samples in a digital format generated fromdownlink signals and/or uplink signals; utilizing at the locate serverround-trip time (RTT) and Time Advance (TA) measurements for positiondetermination and/or accuracy enhancement; utilizing at the locateserver tracking algorithms to improve localization performance andaccuracy; estimating at the locate server time of arrival (TOA), time offlight (TOF) and angle of arrival (AOA) of signals; estimating at thelocate server time difference of arrival (TDOA) between referencesignals from two or more nodes (cells) in the wireless system and/orbetween two or more receive channels; estimating at the locate serverthe AOA for one or more nodes (cells) of the wireless system; utilizingat the locate server joint downlink, uplink and RTT/TA observables forlocalization performance enhancement; and wherein signaling andinformation exchange of the plurality of samples is between the wirelessmobile device, network nodes (eNodeB) of the wireless system, and/ornetwork elements of the wireless system.
 3. The method of claim 1,further comprising producing a data structure that includes theextracted resource elements or I/Q samples before sending the extractedresource elements or I/Q samples to the locate server.
 4. The method ofclaim 1, wherein the signal processing may be applied individually tothe signals from each receive channel and wherein the signal processingincludes a comparison of signal processing results between each receivechannel.
 5. The method of claim 1, wherein the detector or the locateserver is configured to estimate a Doppler shift, utilizing one or moreDoppler specific algorithms and/or techniques, and to utilize one ormore of a multi-path mitigation algorithm and method, including one ormore of an advanced spectrum estimation algorithm, a Constant FalseAlarm Rate (CFAR) detection algorithm, and Space-Time AdaptiveProcessing (STAP).
 6. The method of claim 1, wherein the detector isconfigured to: collect and transfer to the locate server round-trip time(RTT) and Time Advance (TA) measurements performed by a serving celland/or wireless mobile device's receiver; collect metadata, includingeach frame starts and other relevant and/or auxiliary information;buffer the plurality of samples in a digital format generated fromdownlink signals and/or uplink signals; and remove payload samples fromthe plurality of samples to decrease an amount of the data sent to thelocate server.
 7. The method of claim 1, wherein the detector resides ina wireless mobile device and/or a network node (cell).
 8. The method ofclaim 1, wherein the detector is configured to extract digital samplesin time domain.
 9. The method of claim 1, wherein the detector isconfigured to extract digital samples in frequency domain.
 10. Themethod of claim 1, further comprising utilizing at the locate servertracking algorithms to adjust localization performance and accuracy, andwherein the tracking algorithms include one or more of particle filteralgorithms and Kalman filter algorithms.
 11. The method of claim 1,further comprising utilizing at the locate server tracking algorithms toadjust localization performance and accuracy, and wherein a tracker isused to adjust wireless mobile device positioning when the initialwireless mobile device's position is calculated based on RTTmeasurements.
 12. The method of claim 2, further comprising utilizing atthe locate server tracking algorithms to adjust localization performanceand accuracy, and wherein a tracker is used to mitigate thesynchronization error.
 13. The method of claim 1, wherein wirelesssystem is 4.5G, and wherein the locate server is a hosted application ona 4.5G MEC (Mobile Edge Computing) server at an edge of the 4.5Gwireless system.
 14. The method of claim 1, wherein the wireless systemis 5G, and wherein the locate server is hosted in a core networkcomputing cloud of the 5G wireless system.
 15. The method of claim 14,wherein the locate server is configured to support Location-as-a-Service(LaaS) data delivery and the wireless mobile device acts as a gateway tothe core network computing cloud and LaaS for protected physicallocation data.
 16. The method of claim 1, further comprising: one ormore of (a) estimating at the locate server time difference of arrival(TDOA) between reference signals from two or more nodes (cells) in thewireless system and/or between two or more receive channels, (b)utilizing at the locate server round-trip time (RTT) and Time Advance(TA) measurements for position determination and/or accuracyenhancement, and (c) estimating at the locate server time of arrival(TOA), time of flight (TOF) and angle of arrival (AOA) of signals;wherein calculating the location of the wireless mobile device includesone or more of (d) utilizing a multilateration technique/method to solvehyperbolic TDOA based equations, (e) utilizing a triangulationtechnique/method, (f) utilizing RTT together with AOA and/or TDOAobservables, and (g) utilizing a combination of multilateration,triangulation, and/or RTT/receive-transmit timing difference (RTTD). 17.The method of claim 16, wherein utilizing includes iterative and/ornon-iterative algorithms.
 18. The method of claim 16, wherein a set ofavailable TDOA data is split into multiple sets of three or more subsetsand a closed-form solution is found for each subset.
 19. The method ofclaim 18, wherein, location consistency algorithms are utilized tofinalize a position fix for the location of the wireless mobile device.20. The method of claim 17, wherein utilizing includes utilizing uplinkAOA and RTT/TA to estimate an initial location of the wireless mobiledevice based on iterative and non-iterative algorithms.
 21. The methodof claim 1, further comprising one or more of estimating at the locateserver time difference of arrival (TDOA) between reference signals fromtwo or more nodes (cells) in the wireless system and/or between two ormore receive channels, utilizing at the locate server round-trip time(RTT) and Time Advance (TA) measurements for position determinationand/or accuracy enhancement, and estimating at the locate server time ofarrival (TOA), time of flight (TOF) and angle of arrival (AOA) ofsignals; and further comprising determining the location of the wirelessmobile device by scoring intersection points corresponding to each node(cell or sector) in the wireless system, wherein RTT/TA and AOAestimates are not available and only a single TDOA estimate isavailable.
 22. The method of claim 21, wherein the scoring is based on adifference in angles between a direction of a node's antenna subsystemazimuth and a direction to a point on a TDOA based line of bearing and adistance from each intersection point to a corresponding node; andwherein scores may be weighted according to a corresponding node'ssignal to noise ratio (SNR).
 23. The method of claim 1, whereinsignaling and information exchange of the plurality of samples isbetween the wireless mobile device, network nodes (eNodeB) of thewireless system, and/or network elements of the wireless system, andwherein the signaling and information exchange is in line with an OMASUPL protocol and/or a 3GPP LPP/LPPa, or a combination thereof and otherprotocols for communication with the wireless network.
 24. The method ofclaim 1, further comprising: at the detector and/or the locate servergenerating correlation coefficients between reference signals carryingspectrum received from a serving cell of the wireless system and theknown in advance reference signals; utilizing the correlationcoefficients to cancel a serving cell signal in symbols that include theknown in advance reference signals from the serving cell and one or moreneighboring cells of the wireless system; and utilizing a residualsignal left after cancelling the serving cell signal for detection ofreference signals from a neighboring cell signals having a frequencyshift that is the same as a frequency shift of the serving cell.
 25. Themethod of claim 1, further comprising: wherein the signals provided bythe one or more receive channels includes signals from a serving cell ofthe wireless system and signals from one or more neighboring cells andwherein the signals from the serving cell and the signals from one ormore neighboring cells have an equal frequency shift; cancelling thesignals received from the serving cell to create a residual signal; andutilizing the residual signal in detecting the known in advancereference signals from the signals from the one or more neighboringcells.
 26. The method of claim 25, wherein cancelling the signalsreceived from the serving cell includes: generating correlationcoefficients between serving cell reference signals within the signalsfrom the serving cell and the known in advance reference signals withinthe signals received from the one or more neighboring cells; andutilizing the correlation coefficients to cancel the signals from theserving cell in symbols that include the serving cell reference signals.27. The method of claim 1, further comprising: wherein the signalsprovided by the one or more receive channels includes signals from aserving cell of the wireless system and signals from one or moreneighboring cells wherein the reference signal parameters include aknown in advance digital modulation scheme for the symbols; performingchannel equalization based on the known in advance reference signals anddetermining a propagation channel model; equalizing non-referencesignals carried by the digitally modulated symbols with the known inadvance modulation scheme based on the propagation channel model;estimating most likely digitally modulated states of the digitallymodulated symbols used in transmission of the non-reference signalsbased on the known in advance modulation scheme to reconstructconstellation points of the digitally modulated symbols carrying theknown in advance reference signals; generating correlation coefficientsbetween the reconstructed constellation points of signals within thedigitally modulated symbols and serving cell reference signals withinthe signals received from the service cell; utilizing the correlationcoefficients to cancel all of the serving cell signal spectrum insymbols carrying the reference signals with the known in advancemodulation scheme in each subframe resulting in cancellation of thesignals from the serving cell in signals from one or more neighboringcells when the signals from the serving cell and the signals from theone or more neighboring cells that do and do not have an equal frequencyshift and resulting in creation of a residual signal; and utilizing theresidual signal in detecting the known in advance reference signals fromthe signals from the one or more neighboring cells.
 28. The method ofclaim 27, wherein a modulation scheme for the symbols is known inadvance and remains unchanged for a symbol's entire spectrum, includingnon-reference signals.
 29. The method of claim 27, wherein themodulation scheme is based on quadrature modulation.
 30. A method fordetermining a location of a wireless mobile device in a wireless system,the method comprising: at a detector: receiving signals that are notknown in advance and for which there is no advance knowledge of adigital modulation scheme of the received signals; reconstructingconstellation points of digitally modulated signal states that were usedto transmit digitally modulated received signals; generating correlationcoefficients between the received signals of each neighboring cell amongone or more neighboring cells in the wireless system and reconstructedconstellation points of digitally modulated received signals; qualifyingthe correlation coefficients to identify symbols with qualified signals;utilizing the correlation coefficients of the identified symbols tocancel signals corresponding to the identified symbol from the receivedsignal resulting in creation of a residual signal; utilizing theresidual signal in detecting reference signals from the received signalsfrom each neighboring cell; isolating symbols carrying each referencesignal; extracting resource elements or I/Q samples from the symbols ona per symbol basis; and sending the extracted resource elements or I/Qsamples to a locate server physically remote from the detector; at thelocate server: receiving the extracted resource elements or I/Q samplesfrom the detector; estimating locate observables for the wireless mobiledevice based on the extracted resource elements or I/Q samples; andcalculating the location of the wireless mobile device based on theestimated locate observables and extracted resource elements or I/Qsamples.
 31. The method of claim 30, wherein qualifying is based on anSNR estimate of a correlation response in a time domain and wherein aqualification of the SNR estimate is 15 dB or better.
 32. The method ofclaim 30, wherein generating the correlation coefficients is repeatediteratively for previously processed signals to determine if correlationquality improves after later signals are removed from the receivedsignals.
 33. The method of claim 30, wherein the reconstructedconstellation points are part of a forward error correction (FEC)process required to track FEC states.
 34. The method of claim 33,further comprising: zeroing out FEC ambiguous states, including signalsfor one or more symbols producing multiple ambiguous states.
 35. Themethod of claim 30, wherein the received signals are sent by thedetector to the locate server for reconstructing constellation points,generating correlation coefficients, qualifying the correlationcoefficients, utilizing the correlation coefficients, and utilizing theresidual signals.
 36. The method of claim 30, wherein the detectorperforms reconstructing constellation points, generating correlationcoefficients, qualifying the correlation coefficients, utilizing thecorrelation coefficients, and utilizing the residual signals.
 37. Themethod of claim 30, wherein reconstructing constellation points,generating correlation coefficients, qualifying the correlationcoefficients, utilizing the correlation coefficients, and utilizing theresidual signals is performed by the wireless mobile device's receiveron received signals from a serving cell and is performed by the detectorand the locate server in a parallel fashion on received signals from theone or more neighboring cells.
 38. The method of claim 30, whereinreconstructing constellation points, generating correlationcoefficients, qualifying the correlation coefficients, utilizing thecorrelation coefficients, and utilizing the residual signals isperformed by the detector and/or the locate server on received signalsfrom one cell among the serving cell and the one or more neighboringcells at a time or on received signals from the serving cell and the oneor more neighboring cells simultaneously.
 39. The method of claim 30,wherein the reconstructed constellation points of received signals aregenerated by the wireless mobile device's receiver.